@book{Sahubook,
  title = {Bayesian Modeling of Spatio Temporal Data with R},
  author = {Sujit K. Sahu},
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  publisher = {Chapman and Hall},
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  url = {https://doi.org/10.1201/9780429318443},
  year = {2022}
}

@inproceedings{Sahu_rainfall_2020,
author = {Sahu, S. K. and  Bakar, K. S. and  Zhan, J. and  Campbell, J. L. and Yanai, R. D.},
title = {Spatio-temporal Bayesian modeling of precipitation using rain gauge data from the Hubbard Brook Experimental Forest, New Hampshire, USA. },
booktitle ={Proceedings of the Joint Statistical Meetings},
publisher = {American Statistical Association},
pages = {77-92},
year = {2020}
}


@article{Campbell2011,
  author =       {Campbell,  J. L. and Driscoll,  C. T. and Pourmokhtarian,  A. and  Hayhoe,  K.},
  title =        {Streamflow responses to past and projected future changes in climate at the
  Hubbard Brook Experimental Forest, New Hampshire, United States},
  journal =     {Water Resources Research},
  year =         {2011},
  volume =    {47},
  number =    {},
  month =     {},
  pages =     {},
  note =      {},
  doi =   {10.1029/2010WR009438}
}

@techreport{Campbell2012,
  author =       {Campbell,  J. L.  and  Bailey,  A. S. and  Eagar,  C. and  Green,  M. B. and  Battles,  J. J.},
  title =        {Vegetation treatments and hydrologic 563 responses at the Hubbard Brook Experimental Forest, New Hampshire.
        Long-term Silvicultural \&  Ecological Studies: Results for Science and Management.},
  year = {2012},
  series={Technical Report},
  institution = {Global Institute of  Sustainable Forestry, Yale University},
  address = {Yale, Connecticut}
}



@article{Hornbeck1997,
  author =       {Hornbeck,  J. W. and  Martin,  C. W. and Eagar,  C.},
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@article{Campbell2016,
  author =       {John L. Campbell and  Ruth D. Yanai and  Mark B. Green and  Gene E. Likens  and
  Craig R. See and  Amey S. Bailey and  Donald C. Buso and  Daqing Yang},
  title =        {Uncertainty in the net hydrologic flux of calcium in a paired‐watershed harvesting study},
  journal =     {Ecosphere},
  year =         {2016},
  volume =    {},
  number =    {},
  month =     {},
  pages =     {},
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  doi =   {10.1002/ecs2.1299}
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@article{Lindenmayer2009,
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  volume =    {24},
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  month =     {},
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}


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  year =         {2012},
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@techreport{LeeCARBayes2021,
author = {Lee, D.},
year = {2021},
pages = {NA},
title = {CARBayes version 5.2.3: An R Package for Spatial Areal Unit Modelling with Conditional Autoregressive Priors},
url = {https://cran.r-project.org/package=CARBayes},
institution ={University of Glasgow}
}


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year = {2021},
pages = {doi: 10.1002/sim.8897},
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journal = {Statistics in  Medicine}
}

@article{SahuBohning2021,
author = {Sahu, Sujit K. and B\"{o}hning,  Dankmar},
year = {2021},
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title = {Bayesian spatio-temporal joint disease mapping of Covid-19 cases and deaths in local authorities of England},
journal = {Spatial Statistics}
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@incollection{GSC1996,
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 @article{Lee_Rush_Sahu14,
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@article{Ramirez_2020,
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@article {Konstantinoudis2020,
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@article{Campbell2011,
  author =       {Campbell,  J. L. and Driscoll,  C. T. and Pourmokhtarian,  A. and  Hayhoe,  K.},
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  Hubbard Brook Experimental Forest, New Hampshire, United States},
  journal =     {Water Resources Research},
  year =         {2011},
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@techreport{Campbell2012,
  author =       {Campbell,  J. L.  and  Bailey,  A. S. and  Eagar,  C. and  Green,  M. B. and  Battles,  J. J.},
  title =        {Vegetation treatments and hydrologic 563 responses at the Hubbard Brook Experimental Forest, New Hampshire.
        Long-term Silvicultural \&  Ecological Studies: Results for Science and Management.},
  year = {2012},
  series={Technical Report},
  institution = {Global Institute of  Sustainable Forestry, Yale University},
  address = {Yale, Connecticut}
}



@article{Hornbeck1997,
  author =       {Hornbeck,  J. W. and  Martin,  C. W. and Eagar,  C.},
  title =        {Summary of water yield experiments at Hubbard Brook Experimental Forest, New Hampshire},
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  year =         {1997},
  volume =    {27},
  number =    {},
  month =     {},
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}


@article{Campbell2016,
  author =       {John L. Campbell and  Ruth D. Yanai and  Mark B. Green and  Gene E. Likens  and
  Craig R. See and  Amey S. Bailey and  Donald C. Buso and  Daqing Yang},
  title =        {Uncertainty in the net hydrologic flux of calcium in a paired‐watershed harvesting study},
  journal =     {Ecosphere},
  year =         {2016},
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  number =    {},
  month =     {},
  pages =     {},
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}

@article{Lindenmayer2009,
  author =       {Lindenmayer,  D. B. and  Likens, G. E.},
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  number =    {},
  month =     {},
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  note =      {},
  doi =   {10.1016/j.tree.2009.03.005}
}


@article{Sigristel2012,
  author =       {Fabio Sigrist and  Hans R. K\"{u}nsch and Werner A. Stahel},
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}


@article{sahu_bakar_stamet,
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@article {HammondetalGBC2017,
author = {Hammond, M. L. and  Beaulieu, C. and  Sahu, S. K. and  Henson, S. A},
title = {Assessing trends and uncertainties in satellite-era ocean chlorophyll using space-time modeling.},
journal = {Global Biogeochemical Cycles},
volume = {31},
pages = {1103-1117},
year = {2017},
doi = {10.1002/2016GB005600}
}

@article {Berger2006,
author = {James O. Berger},
title = {The Case for Objective Bayesian Analysis},
journal = {Bayesian Analysis},
volume = {1},
number = {3},
pages = {385-402},
year = {2006}
}

@article {Simpsonetal_pc_prior,
author = {Daniel Simpson and  H{\aa}vard Rue and  Andrea Riebler and  Thiago G. Martins  and Sigrunn H. S{\o}rbye},
title = {Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors},
journal = {Statistical Science},
volume = {32},
number = {1},
pages = {1-28},
year = {2017}
}

@book{gomezrubio,
  title={Bayesian inference with INLA},
  author={Virgilio G\'{o}mez-Rubio},
  year={2020},
  publisher={Boca Raton:Chapman and Hall/CRC,}
}


@article {Sahu_Dey_Branco_2003,
author = {Sahu, S. K. and D. K. Dey and Branco, M.},
title = {A new class of multivariate skew distributions with applications to Bayesian regression models},
journal = {Canadian Journal of Statistics},
volume = {31},
number = {},
pages = {129-150},
year = {2003}
}

@incollection{RadfordNealHMC2011,
  author  = {Neal, Radford M.},
  title   = {MCMC Using Hamiltonian Dynamics},
  booktitle={Handbook of Markov Chain Monte Carlo},
  editor = {Steve Brooks and  Andrew Gelman and  Galin L. Jones and Xiao-Li Meng},
  year    = {2011},
  pages = {113-162},
  publisher ={Boca Raton:Chapman and Hall/CRC},
}


@book{blangiardoandcameletti,
  title={Spatial and Spatio-temporal Bayesian Models with R - INLA},
  author={Marta Blangiardo and Michela Cameletti},
  year={2015},
  publisher={Chichester: John Wiley and Sons}
}


@book{Lambertbook2018,
  title={A student's Guide to Bayesian Statistics},
  author={Ben Lambert},
  year={2018},
  publisher={Los Angeles: Sage}
}



@article {Hoffman_Gelman_2014,
author = {Hoffman, M. D. and Gelman, A. },
title = {The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo},
journal = {Journal of Machine Learning Research},
volume = {15},
number = {},
pages = {1593-1623},
year = {2014}
}


@article {bassandsahu2017,
author = {Bass, M. R. and Sahu, S. K.},
title = {A comparison of centering parameterisations of Gaussian process based models for Bayesian computation using MCMC},
journal = {Statistics and Computing},
volume = {27},
number = {},
pages = {1491-1512},
year = {2017}
}




@article{bassandsahu2019,
author = {Mark R. Bass and Sujit K. Sahu},
title = {Dynamically Updated Spatially Varying Parameterizations of Hierarchical Bayesian Models for Spatial Data},
journal = {Journal of Computational and Graphical Statistics},
volume = {28},
number = {1},
pages = {105-116},
year  = {2019},
publisher = {Taylor & Francis},
doi = {10.1080/10618600.2018.1482761},
}


@incollection{GelfandDeyChang,
  author  = {Gelfand, A. E. and Dey, D. K. and Chang, H.},
  title   = {Model determination using predictive distributions with implementation via sampling-based methods.},
  booktitle={Bayesian Statistics 4},
  editor = {J. M. Bernardo and J. O. Berger and A. P. Dawid and A. F. M. Smith},
  year    = {1992},
  pages = {147-167},
  publisher ={Oxford: Oxford University Press},
}


@article {bic1978,
author = {Schwartz, G. E.},
title = {Estimating the dimension of a model},
journal = {Annals of Statistics},
volume = {6},
number = {},
pages = {461-464},
year = {1978}
}



@article{nychkatapering,
author = {Cari G. Kaufman and Mark J. Schervish and Douglas W. Nychka},
title = {Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets},
journal = {Journal of the American Statistical Association},
volume = {103},
number = {484},
pages = {1545-1555},
year  = {2008},
publisher = {Taylor & Francis},
doi = {10.1198/016214508000000959},
URL = {https://doi.org/10.1198/016214508000000959},
eprint = {https://doi.org/10.1198/016214508000000959}
}



@article {Tobler1970,
author = {W. Tobler},
title = {A computer movie simulating urban growth in the Detroit region},
journal = {Economic Geography},
volume = {46},
number = {},
pages = {234-240},
year = {1970}
}


@article {waic2010,
author = {Sumio Watanabe},
title = {Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory},
journal = { Journal of  Machine  Learning  Research},
volume = {11},
number = {},
pages = {3571-3594},
year = {2010}
}


@article {gelman_waic2014,
author = {Andrew Gelman and Jessica Hwang and Aki Vehtari},
title = {Understanding predictive information criteria for Bayesian models},
journal = {Statistics and Computing},
volume = {24},
number = {6},
pages = {997-1016}, doi = {},
year = {2014}
}

@book{Chatfield2003,
  title={The Analysis of Time Series: An Introduction},
  author={Chris Chatfield},
  year={2003},
  publisher={Chapman \& Hall}
}

@book{deBoeck_Wilson2004,
  title={Explanatory Item Response Models},
  author={de Boeck, Paul and Wilson, Mark},
  year={2004},
  publisher={Springer Verlag: New York}
}


@article {VerhagenFox2013,
author = {J. Verhagen and Fox, J.-P.},
title = {Longitudinal measurement in health related surveys. A Bayesian joint growth model for multivariate ordinal responses},
journal = {Statistics in Medicine},
volume = {32},
number = {},
pages = {2988-3005}, doi = {},
year = {2013}
}


@article{spTimer,
   author = {Bakar, Khandoker S. and Sahu, S. K.},
   title = {spTimer: Spatio-Temporal Bayesian Modeling Using R},
   journal = {Journal of Statistical Software, Articles},
   volume = {63},
   number = {15},
   year = {2015},
   keywords = {},
   abstract = {Hierarchical Bayesian modeling of large point-referenced space-time data is increasingly becoming feasible in many environmental applications due to the recent advances in both statistical methodology and computation power. Implementation of these methods using the Markov chain Monte Carlo (MCMC) computational techniques, however, requires development of problem-specific and user-written computer code, possibly in a low-level language. This programming requirement is hindering the widespread use of the Bayesian model-based methods among practitioners and, hence there is an urgent need to develop high-level software that can analyze large data sets rich in both space and time. This paper develops the package spTimer for hierarchical Bayesian modeling of stylized environmental space-time monitoring data as a contributed software package in the R language that is fast becoming a very popular statistical computing platform. The package is able to fit, spatially and temporally predict large amounts of space-time data using three recently developed Bayesian models. The user is given control over many options regarding covariance function selection, distance calculation, prior selection and tuning of the implemented MCMC algorithms, although suitable defaults are provided. The package has many other attractive features such as on the fly transformations and an ability to spatially predict temporally aggregated summaries on the original scale, which saves the problem of storage when using MCMC methods for large datasets. A simulation example, with more than a million observations, and a real life data example are used to validate the underlying code and to illustrate the software capabilities.},
   issn = {1548-7660},
   pages = {1--32},
   doi = {10.18637/jss.v063.i15},
   url = {https://www.jstatsoft.org/v063/i15}
}


@incollection{Salomonetal2003,
  author  = {Salomon, JA and  Mathers, CD and Chatterji, S and  Sadana, R and \"{U}st\"{u}n, TB and Murray, CJL},
  title   = {Quantifying individual levels of health: definitions, concepts and measurement issues},
  booktitle={Health Systems Performance Assessment: Debates, Methods and Empricism},
  editor = {Murray CJL and Evans D},
  chapter = {26},
  year    = {2003},
  pages = {301-318},
  publisher ={Geneva: World Health Organisation},
}


%Salomon JA, Mathers CD, Chatterji S, Sadana R, Ustun TB and Murray CJL. Quantifying individual levels of health: definitions, concepts and measurement issues in Health Systems Performance Assessment: Debates, Methods and Empricism (eds. Murray CJL and Evans D), World Health Organization, Geneva, 2003.


%@article {empty, author = {}, title = {}, journal = {}, volume = {}, number = {}, pages = {}, doi = {}, year = {}, }

%%%%%%%%%%%%%% GCPM %%%%%%%%%%%%%%%%%%%%

@techreport{Medalia2012,
  author =  {Medalia, Carla},
  title =        {Essays on Gender and Health},
  year =         {2012},
  url = {https://repository.upenn.edu/cgi/viewcontent.cgi?article=1704&context=edissertations},
  institution ={University of Pennsylvania},
}




@article{tierney1994,
  author =       {Tierney, L.},
  title =        {Markov Chains for Exploring Posterior Distributions},
  journal =     {Annals of Statistics},
  year =         {1994},
  volume =    {22},
  number =    {4},
  pages =     {1701-1728},
}



@article{Sabariegoetal2015,
  title = {Measuring Disability: Comparing the Impact of Two Data Collection Approaches on Disability Rates},
  author = {Sabariego, C. and  Oberhauser, C. and  Posarac,  A. and  Bickenbach,  J. and  Kostanjsek,  N.  and Chatterji,  S.
and Officer,  A. and  Coenen,  M. and  Chhan,  L. and  Cieza A.},
  year = {2015},
   journal =     {International  Journal of  Environmental  Research and  Public Health},
  volume = {12},
  number = {9},
  pages = {10329--51},
  doi = {10.3390/ijerph120910329}
 }


@article{Kangetal,
  title = {Bias adjustment techniques for improving ozone air quality forecasts.},
  author = {Kang, D. and  Mathur, R. and  Rao, S. T. and  Yu, S.},
  year = {2008},
  journal = {Journal of Geophysical Research},
  volume = {113},
  pages = {10.1029/2008JD010151},
  doi = {10.1029/2008JD010151}
 }


@article{Swaminathan1986,
  title = {Bayesian Estimation in the Three Parameter Logistic Model},
  author = {Swaminathan, H. and Gifford, J. A. },
  year = {1986},
  journal = {Psychometrika},
  volume = {51},
  pages = {589-601},
 }


@article{Mislevy1986,
  title = {Bayes modal estimation in item response models},
  author = {Mislevy, R. J.},
  year = {1986},
  journal = {Psychometrika},
  volume = {51},
  pages = {177-195},
 }




@article{Tsutakawa1986,
  title = {Bayesian Estimation of Item Response Curves},
  author = {Tsutakawa, R. K. and Lin, H. Y.},
  year = {1986},
  journal = {Psychometrika},
  volume = {51},
  pages = {251-267},
 }



@article{Lietal2004,
  title = {Applications of the analytically derived asymptotic standard errors of item response theory item parameter estimates},
  author = {Li, Y. H.  and Lissitz, R. W.},
  year = {2004},
  journal = {Journal of Educational Measurement},
  volume = {41},
  number = {2},
  pages = {85-117},
 }



@article{Ramsay1991,
  title = {Kernal smoothing approaches to nonparametric item characteristic curve estimation},
  author = {Jo Ramsay},
  year = {1991},
  journal = {Psychometrika},
  volume = {56},
  number = {4},
  pages = {611-630},
 }

@article{Ciezaetal2015,
  title = {Marginal Maximum Likelihood Estimation of Item Response Models in R},
  author = {Alarcos Cieza and Cornelia Oberhauser and Jerome Bickenbach and Richard N Jones and Tevfik Bedirhan \"Ust\"un and Nenad Kostanjsek and John N. Morris and Somnath Chatterji},
  year = {2015},
  journal = {International Journal of Epidemiology},
  volume = {44},
 number ={1},
  pages = {229-238},
}

@incollection{Karabatsos2017,
  author  = {Karabatsos, G.},
  title   = {Bayesian nonparametric IRT},
  booktitle={Handbook Of Item Response Theory: Models, Statistical Tools, and Applications, Volume 1},
  editor = {W.J. van der Linden},
  pages   = {Chapter 19},
  year    = {2017},
  publisher ={New York: Taylor \& Francis}
}


@inproceedings{Rasch1961,
  author  = {Rasch, G.},
  title   = {On general laws and the meaning of the measurement in psychology},
  booktitle={Proceedings of the 4th Berkley Symposium on Mathematical Statistics.},
  publisher = {University of California Press},
  pages   = {321-334},
  year    = {1961},
  volume = {4},
}


@incollection{Birnbaum1968,
  author  = {Birnbaum, A.},
  title   = {Some latent trait models and their use in inferring an examinee's ability.},
  booktitle={Statistical Theories of Mental Test Scores},
  editor = {F. M. Lord and M. R. Novick},
  pages   = {397-472},
  year    = {1968},
  publisher ={Addison-Wesley, Reading (Mass)},
}

% url = {https://www.jstatsoft.org/article/view/v020i10/v20i10.pdf}
@article{Johnson2007,
  title = {Marginal Maximum Likelihood Estimation of Item Response Models in {R}},
  author = {Johnson, M. S.},
  year = {2007},
  journal = {Journal of Statistical Software},
  volume = {20},
  number = {10},
  pages = {1-24},
}

%url = {https://www.jstatsoft.org/v17i05/}
@article{Rizopoulos2006,
  title = {ltm: An R Package for Latent Variable Modeling and Item Response Theory Analyses},
  author = {Rizopoulos, D.},
  year = {2006},
  journal = {Journal of Statistical Software},
  volume = {17},
  number = {5},
  pages = {1-25},
}


@article{Glas2005,
  author  = {Glas, C. A. W.},
  title   = {Book review: de Boeck, P., andWilson, M. eds. Explanatory Item ResponseModels: A Generalized
Linear and Nonlinear Approach. New York, NY: Springer, 2004.},
  journal = {Journal of Educational Measurement},
  pages   = {303-307},
  volume  = {42},
  year    = {2005},
}


@article{BockandAitkin1981,
  author  = {Bock, R. D. and  Aitkin, M. },
  title   = {Marginal maximum likelihood estimation of item parameters: Application of an {EM} algorithm.},
  journal = {Psychometrika},
  pages   = {443-459},
  volume  = {46},
  year    = {1981},
}


@article{DuncanMaccEachern2008,
  author  = {Duncan, K. A. and  MacEachern, S. N.},
  title   = {Nonparametric Bayesian modelling for item response},
  journal = {Statistical Modelling},
  pages   = {41-66},
  volume  = {8},
  year    = {2008},
}



@book{Samejima1969,
  title={Estimation of latent ability using a response pattern of graded scores.},
  author={Samejima, F.},
  year={1969},
  publisher={Psychometric Monographs, 17, 1–100.}
}


@incollection{Thissenetal2010,
  author  = {Thissen, David and  Cai, Li and  Bock, R. Darrell},
  title   = {The nominal categories item response model},
  booktitle={Handbook of polytomous item response theory models: Developments and applications},
  editor = {M. Nering and R. Ostini},
  pages   = {43-75},
  year    = {2010},
  publisher ={Routledge}
}



@techreport{Furretal2016,
  author =  {Furr, D. C. and Lee, S. Y. and Lee, J.-H. and Rabe-Hesketh, S.},
  title =        {Two-Parameter Logistic Item Response Model},
  year =         {2016},
  url = {https://mc-stan.org/documentation/case-studies/tutorial_twopl.html},
  institution ={University of California at Berkeley},
}



@article{Sahu2002,
  author  = {Sahu, S. K.},
  title   = {Bayesian Estimation and Model Choice in Item Response Models},
  journal = {Journal of Statistical Computation and Simulation},
  pages   = {217-232},
  volume  = {72},
  year    = {2002},
}


@book{Fox2010,
  title={Bayesian item response modeling: Theory and applications},
  author={Fox, Jean-Paul},
  year={2010},
  publisher={Springer Science \& Business Media}
}

@book{Lord1980,
  title={Applications of item response theory to practical testing problems},
  author={Lord, Frederic M},
  year={1980},
  publisher={Routledge}
}

@book{Rasch1960,
  title={Probabilistic models for some intelligence and attainment tests.},
  author={Rasch, Georg},
  year={1960},
  publisher={Danish Institute for Educational Research, Copenhagen}
}


@article{Masters1982,
author={Masters, Geoff N.},
title={A Rasch model for partial credit scoring},
journal={Psychometrika},
year={1982},
volume={47},
number={2},
pages={149--174},
abstract={A unidimensional latent trait model for responses scored in two or more ordered categories is developed. This ``Partial Credit'' model is a member of the family of latent trait models which share the property of parameter separability and so permit ``specifically objective'' comparisons of persons and items. The model can be viewed as an extension of Andrich's Rating Scale model to situations in which ordered response alternatives are free to vary in number and structure from item to item. The difference between the parameters in this model and the ``category boundaries'' in Samejima's Graded Response model is demonstrated. An unconditional maximum likelihood procedure for estimating the model parameters is developed.},
issn={1860-0980},
doi={10.1007/BF02296272},
url={https://dx.doi.org/10.1007/BF02296272}
}

%url={https://dx.doi.org/10.1007/BF02294839},
@Article{FoxandGlas2001,
author={Fox, Jean-Paul and Glas, Cees A. W.},
title={{Bayesian estimation of a multilevel IRT model using Gibbs sampling}},
journal={Psychometrika},
year={2001},
volume={66},
number={2},
pages={271--288},
issn={1860-0980},
doi={10.1007/BF02294839},
abstract={In this article, a two-level regression model is imposed on the ability parameters in an item response theory (IRT) model. The advantage of using latent rather than observed scores as dependent variables of a multilevel model is that it offers the possibility of separating the influence of item difficulty and ability level and modeling response variation and measurement error. Another advantage is that, contrary to observed scores, latent scores are test-independent, which offers the possibility of using results from different tests in one analysis where the parameters of the IRT model and the multilevel model can be concurrently estimated. The two-parameter normal ogive model is used for the IRT measurement model. It will be shown that the parameters of the two-parameter normal ogive model and the multilevel model can be estimated in a Bayesian framework using Gibbs sampling. Examples using simulated and real data are given.}
}


%URL = {https://apm.sagepub.com/content/30/4/298.abstract},
%eprint = {https://apm.sagepub.com/content/30/4/298.full.pdf+html},

@article{Sinharayetal2006,
author = {Sinharay, Sandip and Johnson, Matthew S. and Stern, Hal S.},
title = {Posterior Predictive Assessment of Item Response Theory Models},
volume = {30},
number = {4},
pages = {298-321},
year = {2006},
doi = {10.1177/0146621605285517},
abstract ={Model checking in item response theory (IRT) is an underdeveloped area. There is no universally accepted tool for checking IRT models. The posterior predictive model-checking method is a popular Bayesian model-checking tool because it has intuitive appeal, is simple to apply, has a strong theoretical basis, and can provide graphical or numerical evidence about model misfit. An important issue with the application of the posterior predictive model-checking method is the choice of a discrepancy measure (which plays a role like that of a test statistic in traditional hypothesis tests). This article examines the performance of a number of discrepancy measures for assessing different aspects of fit of the common IRT models and makes specific recommendations about what measures are most useful in assessing model fit. Graphical summaries of model-checking results are demonstrated to provide useful insights about model fit.},
journal = {Applied Psychological Measurement}
}

%URL = {https://www.jstor.org/stable/20461802},
@article{Sinharay2005,
  author = {Sandip Sinharay},
 journal = {Journal of Educational Measurement},
 number = {4},
 pages = {375-394},
 publisher = {National Council on Measurement in Education, Wiley},
 title = {Assessing Fit of Unidimensional Item Response Theory Models Using a {B}ayesian Approach},
 volume = {42},
 year = {2005},
 ISSN = {00220655, 17453984},
 abstract = {Even though Bayesian estimation has recently become quite popular in item response theory (IRT), there is a lack of works on model checking from a Bayesian perspective. This paper applies the posterior predictive model checking (PPMC) method (Guttman, 1967; Rubin, 1984), a popular Bayesian model checking tool, to a number of real applications of unidimensional IRT models. The applications demonstrate how to exploit the flexibility of the posterior predictive checks to meet the need of the researcher. This paper also examines practical consequences of misfit, an area often ignored in educational measurement literature while assessing model fit.}
}

%url = "https://www.sciencedirect.com/science/article/pii/S0167947313002053",
@article{Bartoluccietal2014,
title = {{MultiLCIRT: An R package for multidimensional latent class item response models}},
journal = {Computational Statistics \& Data Analysis },
volume = {71},
pages = {971 - 985},
year = {2014},
note = "",
issn = "0167-9473",
doi = "https://dx.doi.org/10.1016/j.csda.2013.05.018",
author = {Francesco Bartolucci and Silvia Bacci and Michela Gnaldi},
abstract = {A class of Item Response Theory (IRT) models for binary and ordinal polytomous items is illustrated and an R package for dealing with these models, named MultiLCIRT, is described. The models at issue extend traditional \{IRT\} models allowing for multidimensionality and discreteness of latent traits. They also allow for different parameterizations of the conditional distribution of the response variables given the latent traits, depending on both the type of link function and constraints imposed on the discriminating and difficulty item parameters. These models may be estimated by maximum likelihood via an Expectation–Maximization algorithm, which is implemented in the MultiLCIRT package. Issues related to model selection are also discussed in detail. In order to illustrate this package, two datasets are analyzed: one concerning binary items and referred to the measurement of ability in mathematics and the other one coming from the administration of ordinal polytomous items for the assessment of anxiety and depression. In the first application, aggregation of items in homogeneous groups is illustrated through a model-based hierarchical clustering procedure which is implemented in the proposed package. In the second application, the steps to select a specific model having the best fit in the class of \{IRT\} models at issue are described. }
}

%url = {https://dx.doi.org/10.1002/sim.4475},
@article {LiandBaser2012,
author = {Li, Yuelin and Baser, Ray},
title = {{Using R and WinBUGS to fit a generalized partial credit model for developing and evaluating patient-reported outcomes assessments}},
journal = {Statistics in Medicine},
volume = {31},
number = {18},
publisher = {John Wiley & Sons, Ltd},
issn = {1097-0258},
doi = {10.1002/sim.4475},
pages = {2010--2026},
keywords = {item response theory, generalized partial credit model, Rasch model},
year = {2012},
abstract = {The US Food and Drug Administration recently announced the final guidelines on the development and validation of patient-reported outcomes (PROs) assessments in drug labeling and clinical trials. This guidance paper may boost the demand for new PRO survey questionnaires. Henceforth, biostatisticians may encounter psychometric methods more frequently, particularly item response theory (IRT) models to guide the shortening of a PRO assessment instrument. This article aims to provide an introduction on the theory and practical analytic skills in fitting a generalized partial credit model (GPCM) in IRT. GPCM theory is explained first, with special attention to a clearer exposition of the formal mathematics than what is typically available in the psychometric literature. Then, a worked example is presented, using self-reported responses taken from the international personality item pool. The worked example contains step-by-step guides on using the statistical languages r and WinBUGS in fitting the GPCM. Finally, the Fisher information function of the GPCM model is derived and used to evaluate, as an illustrative example, the usefulness of assessment items by their information contents. This article aims to encourage biostatisticians to apply IRT models in the re-analysis of existing data and in future research. Copyright © 2012 John Wiley & Sons, Ltd.},
}



%url={https://dx.doi.org/10.3758/s13428-014-0540-5}
@article{Johnsonetal2015,
author={Johnson, Timothy R. and Kuhn, Kristine M.},
title={Simulation-based {B}ayesian inference for latent traits of item response models: Introduction to the {l}tbayes package for {R}},
journal={Behavior Research Methods},
year={2015},
volume={47},
number={4},
pages={1309--1327},
abstract={This paper introduces the ltbayes package for R. This package includes a suite of functions for investigating the posterior distribution of latent traits of item response models. These include functions for simulating realizations from the posterior distribution, profiling the posterior density or likelihood function, calculation of posterior modes or means, Fisher information functions and observed information, and profile likelihood confidence intervals. Inferences can be based on individual response patterns or sets of response patterns such as sum scores. Functions are included for several common binary and polytomous item response models, but the package can also be used with user-specified models. This paper introduces some background and motivation for the package, and includes several detailed examples of its use.},
issn={1554-3528},
doi={10.3758/s13428-014-0540-5},
}



@article{Huangetal2013,
author = {Huang, Hung-Yu and Wang, Wen-Chung and Chen, Po-Hsi and Su, Chi-Ming},
title = {Higher-Order Item Response Models for Hierarchical Latent Traits},
volume = {37},
number = {8},
pages = {619-637},
year = {2013},
doi = {10.1177/0146621613488819},
abstract ={Many latent traits in the human sciences have a hierarchical structure. This study aimed to develop a new class of higher order item response theory models for hierarchical latent traits that are flexible in accommodating both dichotomous and polytomous items, to estimate both item and person parameters jointly, to allow users to specify customized item response functions, and to go beyond two orders of latent traits and the linear relationship between latent traits. Parameters of the new class of models can be estimated using the Bayesian approach with Markov chain Monte Carlo methods. Through a series of simulations, the authors demonstrated that the parameters in the new class of models can be well recovered with the computer software WinBUGS, and the joint estimation approach was more efficient than multistaged or consecutive approaches. Two empirical examples of achievement and personality assessments were given to demonstrate applications and implications of the new models.},
URL = {https://apm.sagepub.com/content/37/8/619.abstract},
eprint = {https://apm.sagepub.com/content/37/8/619.full.pdf+html},
journal = {Applied Psychological Measurement}
}

@article{Glasetal2003,
author = {Glas, Cees A. W. and Meijer, Rob R.},
title = {A {B}ayesian Approach to Person Fit Analysis in Item Response Theory Models},
volume = {27},
number = {3},
pages = {217-233},
year = {2003},
doi = {10.1177/0146621603027003003},
abstract ={A Bayesian approach to the evaluation of person fit in item response theory (IRT) models is presented. In a posterior predictive check, the observed value on a discrepancy variable is positioned in its posterior distribution. In a Bayesian framework, a Markov chain Monte Carlo procedure can be used to generate samples of the posterior distribution of the parameters of interest. These draws can also be used to compute the posterior predictive distribution of the discrepancy variable. The procedure is worked out in detail for the three-parameter normal ogive model, but it is also shown that the procedure can be directly generalized to many other IRT models. Type I error rate and the power against some specific model violations are evaluated using a number of simulation studies. Index terms: Bayesian statistics, item response theory, person fit, model fit, 3-parameter normal ogive model, posterior predictive check, power studies, Type I error.},
journal = {Applied Psychological Measurement}
}



@article{Curtis2010,
  title={{BUGS} code for item response theory},
  author={Curtis, S McKay},
  journal={Journal of Statistical Software},
  volume={36},
  number={1},
  pages={1--34},
  year={2010},
  publisher={American Statistical Association},
  abstract={I present BUGS code to fit common models from item response theory (IRT), such
as the two parameter logistic model, three parameter logisitic model, graded response
model, generalized partial credit model, testlet model, and generalized testlet models. I
demonstrate how the code in this article can easily be extended to it more complicated
IRT models, when the data at hand require a more sophisticated approach. Specifically,
I describe modifications to the BUGS code that accommodate longitudinal item response
data.
}
}

@article{Albert1992,
author = {Albert, James H.},
title = {Bayesian Estimation of Normal Ogive Item Response Curves Using {G}ibbs Sampling},
volume = {17},
number = {3},
pages = {251-269},
year = {1992},
doi = {10.3102/10769986017003251},
abstract ={The problem of estimating item parameters from a two-parameter normal ogive model is considered. Gibbs sampling (Gelfand & Smith, 1990) is used to simulate draws from the joint posterior distribution of the ability and item parameters. This method gives marginal posterior density estimates for any parameter of interest; these density estimates can be used to judge the accuracy of normal approximations based on maximum likelihood estimates. This simulation technique is illustrated using data from a mathematics placement exam.},
journal = {Journal of Educational and Behavioral Statistics}
}



@article{FalkandCai2016,
  title={Maximum marginal likelihood estimation of a monotonic polynomial generalized partial credit model with applications to multiple group analysis},
  author={Falk, Carl F and Cai, Li},
  journal={Psychometrika},
  volume = {81},
  number = {2},
  pages={434--460},
  year={2016},
  publisher={Springer},
  abstract = {We present a semi-parametric approach to estimating item response functions (IRF) useful when the true IRF does not strictly follow commonly used functions. Our approach replaces the linear predictor of the generalized partial credit model with a monotonic polynomial. The model includes the regular generalized partial credit model at the lowest order polynomial. Our approach extends Liang's (A semi-parametric approach to estimate IRFs, Unpublished doctoral dissertation, 2007) method for dichotomous item responses to the case of polytomous data. Furthermore, item parameter estimation is implemented with maximum marginal likelihood using the Bock--Aitkin EM algorithm, thereby facilitating multiple group analyses useful in operational settings. Our approach is demonstrated on both educational and psychological data. We present simulation results comparing our approach to more standard IRF estimation approaches and other non-parametric and semi-parametric alternatives.. }
}



@article{Mengetal2014,
author = {Xiang-Bin Meng and Jian Tao and Ning-Zhong Shi},
title = {An item response model for {L}ikert-type data that incorporates response time in personality measurements},
journal = {Journal of Statistical Computation and Simulation},
volume = {84},
number = {1},
pages = {1-21},
year = {2014},
doi = {10.1080/00949655.2012.692368},
abstract = { This study defines a measurement index for the differences in response probabilities to an item. Based on the difference index, a probability–difficulty (PD) hypothesis is proposed. A general framework for modelling responses and response times (RTs) on Likert-type personality items is presented, in which the submodel describing the item responses can be a graded item response theory model, and the submodel describing RTs is developed based on the PD hypothesis. This framework is exemplified by employing the generalized partial credit model for responses and a log-normal model for RTs. Furthermore, Bayesian methods for estimating model parameters and for assessing the model–data fit are described. A simulation study shows that the new approach improves the accuracy of estimating the individual trait levels with the ancillary information contained in RTs. Finally, the applicability of our approach is illustrated by an empirical example in personality measurements. }
}



@article{PatzandJunker1999,
 author = {Richard J. Patz and Brian W. Junker},
 journal = {Journal of Educational and Behavioral Statistics},
 number = {4},
 pages = {342-366},
 publisher = {[American Educational Research Association, Sage Publications, Inc., American Statistical Association]},
 title = {Applications and Extensions of {MCMC} in {IRT}: Multiple Item Types, Missing Data, and Rated Responses},
 volume = {24},
 year = {1999},
 ISSN = {10769986, 19351054},
  abstract = {Patz and Junker (1999) describe a general Markov chain Monte Carlo (MCMC) strategy, based on Metropolis-Hastings sampling, for Bayesian inference in complex item response theory (IRT) settings. They demonstrate the basic methodology using the two-parameter logistic (2PL) model. In this paper we extend their basic MCMC methodology to address issues such as non-response, designed missingness, multiple raters, guessing behavior and partial credit (polytomous) test items. We apply the basic MCMC methodology to two examples from the National Assessment of Educational Progress 1992 Trial State Assessment in Reading: (a) a multiple item format (2PL, 3PL, and generalized partial credit) subtest with missing response data; and (b) a sequence of rated, dichotomous short-response items, using a new IRT model called the generalized linear logistic test model (GLLTM).}
}

@article{Muraki1992,
author = {Muraki, Eiji},
title = {A Generalized Partial Credit Model: Application of an {EM} Algorithm},
volume = {16},
number = {2},
pages = {159-176},
year = {1992},
doi = {10.1177/014662169201600206},
abstract ={The partial credit model (PCM) with a varying slope parameter is developed and called the generalized partial credit model (GPCM). The item step parameter of this model is decomposed to a location and a threshold parameter, following Andrich's (1978) rating scale formulation. The EM algorithm for estimating the model parameters is derived. The performance of this generalized model is compared on both simulated and real data to a Rasch family of polytomous item response models. Simulated data were generated and then analyzed by the various polytomous item response models. The results demonstrate that the rating formulation of the GPCM is quite adaptable to the analysis of polytomous item responses. The real data used in this study consisted of the National Assessment of Educational Progress (Johnson & Allen, 1992) mathematics data that used both dichotomous and polytomous items. The PCM was applied to these data using both constant and varying slope parameters. The GPCM, which provides for varying slope parameters, yielded better fit to the data than did the PCM.},
journal = {Applied Psychological Measurement}
}




@article{Muraki1993,
author = {Muraki, Eiji},
title = {Information functions of the generalized partial credit model},
volume = {17},
pages = {351-363},
year = {1993},
journal = {Applied Psychological Measurement},
}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

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@article {Shaddick_Policy_2013,
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@techreport {Connolly_Fuller_Defra2013,
author = {Connonlly, E. and Fuller, G. and  Baker, T. and Willis P.},
title = {Update on Implementation of the Daily Air Quality Index},
institution = {Department for Environment Food and Rural Affairs},
year = {2013},
}


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doi = {},
year = {2014},
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@article {Schneideretal2009,
author = {A Schneider and M A Friedl and D Potere},
title = {A new map of global urban extent from MODIS satellite data},
journal = {Environmental Research Letters},
volume = {4},
number = {4},
pages = {044003},
doi = {10.1088/1748-9326/4/4/044003},
year = {2009},
}
@article {Lindgrenetal2011,
author = {Lindgren, Finn and Rue, H{\aa}vard and Lindstr\"{o}m, Johan},
title = {An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach},
journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
volume = {73},
number = {4},
publisher = {Blackwell Publishing Ltd},
issn = {1467-9868},
url = {https://dx.doi.org/10.1111/j.1467-9868.2011.00777.x},
doi = {10.1111/j.1467-9868.2011.00777.x},
pages = {423--498},
keywords = {Approximate Bayesian inference, Covariance functions, Gaussian fields, Gaussian Markov random fields, Latent Gaussian models, Sparse matrices, Stochastic partial differential equations},
year = {2011},
}

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author = {Bolin, D. and  Lindgren, Finn},
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year = {2011},
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author = {Fuglstad, Geir-Arne and  Simpson, Daniel Peter and  Lindgren, Finn Kristian and  Rue, Håvard},
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@techreport{SahuandMukhopadhyay2015,
  author =       {Sahu, S. K.  and Mukhopadhyay, S.},
  title =        {On generating a flexible class of anisotropic spatial models using Gaussian predictive processes},
  journal =     {Technical Report, University of Southampton},
  year =         {2015},
  institution = {University of Southampton},
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@article{Piranietal2014,
Author = {Pirani, Monica and Gulliver, John and Fuller, Gary W. and Blangiardo,
   Marta},
Title = {{Bayesian spatiotemporal modelling for the assessment of short-term
   exposure to particle pollution in urban areas}},
Journal = {{Journal of Exposure Science and Environmental Epidemiology}},
Year = {{2014}},
Volume = {{24}},
Number = {{3}},
Pages = {{319-327}},
Month = {{MAY-JUN}},
Abstract = {{This paper describes a Bayesian hierarchical approach to predict
   short-term concentrations of particle pollution in an urban environment,
   with application to inhalable particulate matter (PM10) in Greater
   London. We developed and compared several spatiotemporal models that
   differently accounted for factors affecting the spatiotemporal
   properties of particle concentrations. We considered two main source
   contributions to ambient measurements: (i) the long-range transport of
   the secondary fraction of particles, which temporal variability was
   described by a latent variable derived from rural concentrations; and
   (ii) the local primary component of particles (traffic- and
   non-traffic-related) captured by the output of the dispersion model
   ADMS-Urban, which site-specific effect was described by a Bayesian
   kriging. We also assessed the effect of spatiotemporal covariates,
   including type of site, daily temperature to describe the seasonal
   changes in chemical processes affecting local PM10 concentrations that
   are not considered in local-scale dispersion models and day of the week
   to account for time-varying emission rates not available in emissions
   inventories. The evaluation of the predictive ability of the models,
   obtained via a cross-validation approach, revealed that concentration
   estimates in urban areas benefit from combining the city-scale particle
   component and the long-range transport component with covariates that
   account for the residual spatiotemporal variation in the pollution
   process.}},
DOI = {{10.1038/jes.2013.85}},
ISSN = {{1559-0631}},
EISSN = {{1559-064X}},
Unique-ID = {{ISI:000334571000012}},
}
@article{ShaddickandWakefield2002,
Author = {Shaddick, G and Wakefield, J},
Title = {{Modelling daily multivariate pollutant data at multiple sites}},
Journal = {{Journal of the royal Statistical Society, Series C - Applied Statistics}},
Year = {{2002}},
Volume = {{51}},
Number = {{3}},
Pages = {{351-372}},
Abstract = {{This paper considers the spatiotemporal modelling of four pollutants
   measured daily at eight monitoring sites in London over a 4-year period.
   Such multiple-pollutant data sets measured over time at multiple sites
   within a region of interest are typical. Here, the modelling was carried
   out to provide the exposure for a study investigating the health effects
   of air pollution. Alternative objectives include the design problem of
   the positioning of a new monitoring site, or for regulatory purposes to
   determine whether environmental standards are being met. In general,
   analyses are hampered by missing data due, for example, to a particular
   pollutant not being measured at a site, a monitor being inactive by
   design (e.g. a 6-day monitoring schedule) or because of an unreliable or
   faulty monitor. Data of this type are modelled here Within a dynamic
   linear modelling framework, in which the dependences across time, space
   and pollutants are exploited. Throughout the approach is Bayesian, with
   implementation via Markov chain Monte Carlo sampling.}},
DOI = {{10.1111/1467-9876.00273}},
ISSN = {{0035-9254}},
ResearcherID-Numbers = {{Shaddick, Gavin/B-3344-2011}},
Unique-ID = {{ISI:000177211300006}},
}

@article{LeeandShaddick2010,
Author = {Lee, Duncan and Shaddick, Gavin},
Title = {{Spatial Modeling of Air Pollution in Studies of Its Short-Term Health
   Effects}},
Journal = {{Biometrics}},
Year = {{2010}},
Volume = {{66}},
Number = {{4}},
Pages = {{1238-1246}},
Abstract = {{P>In studies that estimate the short-term effects of air pollution on
   health, daily measurements of pollution concentrations are often
   available from a number of monitoring locations within the study area.
   However, the health data are typically only available in the form of
   daily counts for the entire area, meaning that a corresponding single
   daily measure of pollution is required. The standard approach is to
   average the observed measurements at the monitoring locations, and use
   this in a log-linear health model. However, as the pollution surface is
   spatially variable this simple summary is unlikely to be an accurate
   estimate of the average pollution concentration across the region, which
   may lead to bias in the resulting health effects. In this article, we
   propose an alternative approach that jointly models the pollution
   concentrations and their relationship with the health data using a
   Bayesian spatio-temporal model. We compare this approach with the simple
   spatial average using a simulation study, by investigating the impact of
   spatial variation, monitor placement, and measurement error in the
   pollution data. An epidemiological study from Greater London is then
   presented, which estimates the relationship between respiratory
   mortality and four different pollutants.}},
DOI = {{10.1111/j.1541-0420.2009.01376.x}},
ISSN = {{0006-341X}},
ResearcherID-Numbers = {{Shaddick, Gavin/B-3344-2011}},
Unique-ID = {{ISI:000285204900026}},
}

@article{Nealetal2014,
Author = {Neal, L. S. and Agnew, P. and Moseley, S. and Ordonez, C. and Savage, N.
   H. and Tilbee, M.},
Title = {{Application of a statistical post-processing technique to a gridded,
   operational, air quality forecast}},
Journal = {{ATMOSPHERIC ENVIRONMENT}},
Year = {{2014}},
Volume = {{98}},
Pages = {{385-393}},
Month = {{DEC}},
Abstract = {{An automated air quality forecast bias correction scheme based on the
   short-term persistence of model bias with respect to recent observations
   is described. The scheme has been implemented in the operational Met
   Office five day regional air quality forecast for the UK. It has been
   evaluated against routine hourly pollution observations for a year-long
   hindcast The results demonstrate the value of the scheme in improving
   performance. For the first day of the forecast the post-processing
   reduces the bias from 7.02 to 0.53 mu g m(-3) for O-3, from -4.70 to
   -0.63 mu g m(-3) for NO2, from -4.00 to -0.13 mu g m(-3) for PM2.5 and
   from -7.70 to -0.25 mu g m(-3) for PM10. Other metrics also improve for
   all species. An analysis of the variation of forecast skill with
   lead-time is presented and demonstrates that the post-processing
   increases forecast skill out to five days ahead. Crown Copyright (C)
   2014 Published by Elsevier Ltd. All rights reserved.}},
DOI = {{10.1016/j.atmosenv.2014.09.004}},
ISSN = {{1352-2310}},
EISSN = {{1873-2844}},
Unique-ID = {{ISI:000345475000041}},
}
@article{DeIacoetal,
Author = {De Iaco, S and Myers, DE and Posa, D},
Title = {{Space-time variograms and a functional form for total air pollution
   measurements}},
Journal = {{COMPUTATIONAL STATISTICS \& DATA ANALYSIS}},
Year = {{2002}},
Volume = {{41}},
Number = {{2}},
Pages = {{311-328}},
Month = {{DEC 28}},
Abstract = {{A space-time functional form for some contaminants is obtained and used
   for estimating total air pollution (TAP) in the district of Milan,
   Italy, during selected high-risk days of 1999. This functional form is
   determined through a space-time product-sum variogram model for TAP
   measurements and the dual form of kriging, i.e., radial basis functions.
   Data for nitric oxide (NO), nitrogen dioxide (NO2) and carbon monoxide
   (CO) collected in Milan district, Italy are used to generate a combined
   indicator of traffic pollution, called TAP. In a previous study the
   weightings were obtained by multiple principal component analyses of the
   daily concentration levels. It was found that the first component
   explains approximately 70\% of the total variance for each day and this
   component is treated as samples defined over space and time. A
   systematic pattern, which follows the corridor along which survey
   stations, characterized by heavy traffic are located, has been observed
   for TAP throughout Milan district, for all days considered. Note that
   the pollution data set is just an illustration for the new, statistical
   method proposed. (C) 2002 Elsevier Science B.V. All rights reserved.}},
DOI = {{10.1016/S0167-9473(02)00081-6}},
ISSN = {{0167-9473}},
Unique-ID = {{ISI:000179937400004}},
}

@article{McMillanetal2005,
Author = {McMillan, N and Bortnick, SM and Irwin, ME and Berliner, LM},
Title = {{A hierarchical Bayesian model to estimate and forecast ozone through
   space and time}},
Journal = {{Atmospheric Environment}},
Year = {{2005}},
Volume = {{39}},
Number = {{8}},
Pages = {{1373-1382}},
Month = {{MAR}},
Abstract = {{A Bayesian hierarchical regime switching model describing the
   spatial-temporal behavior of ozone (O(3)) within a domain covering Lake
   Michigan during spring-summer 1999 is developed. The model incorporates
   linkages between ozone and meteorology. It is specifically formulated to
   identify meteorological regimes conducive of high ozone levels and allow
   ozone behavior during these periods to be different from typical ozone
   behavior. The model is used to estimate or forecast spatial fields of
   O(3) conditional on observed (or forecasted) meteorology including
   temperature, humidity, pressure, and wind speed and direction. The model
   is successful at forecasting the onset of periods of high ozone levels,
   but more work is needed to also accurately identify departures from
   these periods. (c) 2004 Elsevier Ltd. All rights reserved.}},
DOI = {{10.1016/j./atmosenv.2004.10.047}},
ISSN = {{1352-2310}},
Unique-ID = {{ISI:000227703200001}},
}

@article{Cocchi2007,
Author = {Cocchi, Daniela and Greco, Fedele and Trivisano, Carlo},
Title = {{Hierarchical space-time modelling of PM10 pollution}},
Journal = {{ATMOSPHERIC ENVIRONMENT}},
Year = {{2007}},
Volume = {{41}},
Number = {{3}},
Pages = {{532-542}},
Month = {{JAN}},
Abstract = {{In this paper, we propose a hierarchical spatio-temporal model for daily
   mean concentrations of PM10 pollution. The main aims of the proposed
   model are the identification of the sources of variability
   characterising the PM10 process and the estimation of pollution levels
   at unmonitored spatial locations. We adopt a fully Bayesian approach,
   using Monte Carlo Markov Chain algorithms. We apply the model on PM10
   data measured at 11 monitoring sites located in the major towns and
   cities of Italy's Emilia-Romagna Region. The model is designed for areas
   with PM10 measurements available; the case of PM10 level estimation from
   emissions data is not handled. The model has been carefully checked
   using Bayesian p-values and graphical posterior predictive checks.
   Results show that the temporal random effect is the most important when
   explaining PM10 levels. (c) 2006 Elsevier Ltd. All rights reserved.}},
DOI = {{10.1016/j.atmosenv.2006.08.032}},
ISSN = {{1352-2310}},
Unique-ID = {{ISI:000243831000007}},
}


@article{Alkuwarietal2013,
Author = {Alkuwari, Farha A. and Guillas, Serge and Wang, Yuhang},
Title = {{Statistical downscaling of an air quality model using Fitted Empirical
   Orthogonal Functions}},
Journal = {{Atmospheric Environment}},
Year = {{2013}},
Volume = {{81}},
Pages = {{1-10}},
Month = {{DEC}},
Abstract = {{Downscaling is a technique that is used to extract high-resolution
   information from regional scale variables produced by coarse resolution
   models such as Chemical Transport Models (CTMs). Statistical downscaling
   methods in geophysics often rely on Empirical Orthogonal Functions
   (E0F5). EOFs are spatial Principal Components (PCs) that display
   space-time modes of variability of a quantity over a region. Here we
   present a novel statistical downscaling method that employs Fitted
   Empirical Orthogonal Functions (F-EOFs) to provide local forecasts.
   F-EOFs differ from EOFs in that they represent spacetime variations
   associated with a particular location through the use of inverse
   regression. We illustrate our downscaling method, for ozone levels over
   the US, with the Regional chEmical trAnsport Model (REAM) whose outputs
   are over 70 by 70 km grid cells. We use ground level ozone observations
   from monitoring stations within the south-eastern US region to downscale
   REAM. We select the first leading F-E0F5 and regress our observations on
   the corresponding F-EOF loadings. We also compare our results to linear
   regression and PC regression. The regression on F-EOFs shows the best
   predictive ability. To examine the consistency of our results we repeat
   the analysis for different fitting and validation periods. Furthermore,
   in our application, PFC regression also outperforms PC regression as a
   dimension reduction technique. (C) 2013 Elsevier Ltd. All rights
   reserved.}},
DOI = {{10.1016/j.atmosenv.2013.08.031}},
ISSN = {{1352-2310}},
EISSN = {{1873-2844}},
ResearcherID-Numbers = {{Wang, Yuhang/B-5578-2014}},
Unique-ID = {{ISI:000329377600001}},
}

@article{ Beckermanetal2013,
Author = {Beckerman, Bernardo S. and Jerrett, Michael and Serre, Marc and Martin,
   Randall V. and Lee, Seung-Jae and van Donkelaar, Aaron and Ross, Zev and
   Su, Jason and Burnett, Richard T.},
Title = {{A Hybrid Approach to Estimating National Scale Spatiotemporal
   Variability of PM2.5 in the Contiguous United States}},
Journal = {{ENVIRONMENTAL SCIENCE \& TECHNOLOGY}},
Year = {{2013}},
Volume = {{47}},
Number = {{13}},
Pages = {{7233-7241}},
Month = {{JUL 2}},
Abstract = {{Airborne fine particulate matter exhibits spatiotemporal variability at
   multiple scales, which presents challenges to estimating exposures for
   health effects assessment. Here we created a model to predict ambient
   particulate matter less than 2.5 mu m in aerodynamic diameter (PM2.5)
   across the contiguous United States to be applied to health effects
   modeling. We developed a hybrid approach combining a land use regression
   model (LUR) selected with a machine learning method, and Bayesian
   Maximum Entropy (BME) interpolation of the LUR space-time residuals. The
   PM2.5 data set included 104,172 monthly observations at 1464 monitoring
   locations with approximately 10\% of locations reserved for cross
   validation LUR models were based on remote sensing estimates of PM2.5,
   land use and traffic indicators. Normalized cross validated R-2 values
   for LUR were 0.63 and 0.11 with and without remote sensing,
   respectively, suggesting remote sensing is a strong predictor of ground
   level concentrations In the models including the BME interpolation of
   the residuals, cross validated R-2 were 0.79 for both configurations;
   the model without remotely sensed data described more line scale
   variation than the model including remote sensing. Our results suggest
   that our modeling framework can predict ground level concentrations of
   PM2.5 at multiple scales over the contiguous U.S.}},
DOI = {{10.1021/es400039u}},
ISSN = {{0013-936X}},
ResearcherID-Numbers = {{Martin, Randall/C-1205-2014}},
ORCID-Numbers = {{Martin, Randall/0000-0003-2632-8402}},
Unique-ID = {{ISI:000321521400063}},
}

@article{lecouretal,
Author = {Lecoeur, Eve and Seigneur, Christian and Page, Christian and Terray,
   Laurent},
Title = {{A statistical method to estimate PM2.5 concentrations from meteorology
   and its application to the effect of climate change}},
Journal = {{JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES}},
Year = {{2014}},
Volume = {{119}},
Number = {{6}},
Pages = {{3537-3585}},
Month = {{MAR 27}},
Abstract = {{A statistical algorithm was developed to estimate PM2.5 concentrations  over Europe based on a weather-type representation of the meteorology.
   We used modeled PM2.5 concentrations as pseudoobservations, because of a
   lack of PM2.5 speciated measurements over Europe, and included four
   meteorological variables. This algorithm was evaluated on the learning
   period (2000-2008) to test its ability to reproduce the pseudoobserved
   data set and then applied for two climatological scenarios (RCP4.5 and
   RCP8.5) and one historical (1975-2004) and two future periods (2020-2049
   and 2070-2099). In Italy, Poland, and northern, eastern, and
   southeastern Europe, all future scenarios lead to decreases in PM2.5,
   whereas in the Balkans, Benelux, the UK, and northern France, they lead
  to increases in PM2.5. Considering each season separately shows stronger
   responses, which may vary for a given region and scenario. Decomposing
   the changes in PM2.5 concentrations as the sum of intertype and
   intratype changes, and a residual term shows that (1) the residual term
   is negligible; (2) intertype changes affect more the regions along the
   Atlantic Ocean; and (3) in most other regions, intertype and intratype
   changes are often on the same order of magnitude. The relationship
   between the atmospheric circulation and weather types evolves and
   therefore modifies the mean of meteorological variables and PM2.5
   concentrations. This algorithm offers a novel approach to investigate
   the effect of climate change on air quality and can be applied to other
   pollutants, regions, and meteorological models. Furthermore, this
   approach can be applied using actual speciated PM2.5 observations, if a
   sufficiently dense monitoring network were available.}},
DOI = {{10.1002/2013JD021172}},
ISSN = {{2169-897X}},
EISSN = {{2169-8996}},
Unique-ID = {{ISI:000336046600050}},
}


@article{Chemeletal,
Author = {Chemel, C. and Fisher, B. E. A. and Kong, X. and Francis, X. V. and
   Sokhi, R. S. and Good, N. and Collins, W. J. and Folberth, G. A.},
Title = {{Application of chemical transport model CMAQ to policy decisions
   regarding PM2.5 in the UK}},
Journal = {{ATMOSPHERIC ENVIRONMENT}},
Year = {{2014}},
Volume = {{82}},
Pages = {{410-417}},
Month = {{JAN}},
Abstract = {{This paper shows how the advanced chemical transport model CMAQ can be
   used to estimate future levels of PM2.5 in the UK, the key air pollutant
   in terms of human health effects, but one which is largely made up from
   the formation of secondary particulate in the atmosphere. By adding the
   primary particulate contribution from typical urban roads and including
   a margin for error, it is concluded that the current indicative limit
   value for PM2.5 will largely be met in 2020 assuming 2006 meteorological
   conditions. Contributions to annual average regional PM2.5 concentration
   from wild fires in Europe in 2006 and from possible climate change
  between 2006 and 2020 are shown to be small compared with the change in
   PM2.5 concentration arising from changes in emissions between 2006 and
  2020. The contribution from emissions from major industrial sources
   regulated in the UK is estimated from additional CMAQ calculations. The
   potential source strength of these emissions is a useful indicator of
   the linearity of the response of the atmosphere to changes in emissions.
   Uncertainties in the modelling of regional and local sources are taken
   into account based on previous evaluations of the models. Future actual
   trends in emissions mean that exceedences of limit values may arise, and
   these and further research into PM2.5 health effects will need to be
   part of the future strategy to manage PM2.5 concentrations. (C) 2013
   Elsevier Ltd. All rights reserved.}},
DOI = {{10.1016/j.atmosenv.2013.10.001}},
ISSN = {{1352-2310}},
EISSN = {{1873-2844}},
ResearcherID-Numbers = {{Collins, William/A-5895-2010}},
ORCID-Numbers = {{Collins, William/0000-0002-7419-0850}},
Unique-ID = {{ISI:000329886200043}},
}

@article{PaciorekandSchervish2006,
Author = {Paciorek, C. J. and Schervish, M.},
Title = {Spatial Modelling using a new class of covariance functions},
Journal = {{Environmetrics}},
Year = {{2006}},
Volume = {{17}},
Number = {{}},
Pages = {{483-506}},
}

@article{Higdon1998,
Author = {Higdon, D},
Title = {{A process-convolution approach to modelling temperatures in the North
   Atlantic Ocean}},
Journal = {{Environmental and Ecological Statistics}},
Year = {{1998}},
Volume = {{5}},
Number = {{2}},
Pages = {{173-190}},
Month = {{JUN}},
DOI = {{10.1023/A:1009666805688}},
ISSN = {{1352-8505}},
Unique-ID = {{ISI:000076328200005}},
}

@article{SchmidtandOhagan2003,
Author = {Schmidt, AM and O'Hagan, A},
Title = {{Bayesian inference for non-stationary spatial covariance structure via
   spatial deformations}},
Journal = {{Journal of the Royal Statistical Society, Series B}},
Year = {{2003}},
Volume = {{65}},
Number = {{3}},
Pages = {{743-758}},
DOI = {{10.1111/1467-9868.00413}},
ISSN = {{1369-7412}},
ResearcherID-Numbers = {{Schmidt, Alexandra/O-2301-2013}},
ORCID-Numbers = {{Schmidt, Alexandra/0000-0002-6448-6367}},
Unique-ID = {{ISI:000184473200010}},
}

@article{Gelfandetal2004,
Author = {Gelfand, A. E. and Schmidt, A. M. and  Banerjee, S. and Sirmans, C. F.},
Title = {Nonstationary Multivariate Process Modelling through Spatially Varying
Coregionalization (with discussion)},
Journal = {{Test}},
Year = {{2004}},
Volume = {{2}},
Number = {{}},
Pages = {{1-50}},
}


@article{SampsonandGuttorp1992,
Author = {Sampson, PD and Guttorp, P},
Title = {{Nonparametric estimation of nonstationary spatial covariance structure}},
Journal = {{Journal of the American Statistical Association}},
Year = {{1992}},
Volume = {{87}},
Number = {{417}},
Pages = {{108-119}},
Month = {{MAR}},
Publisher = {{AMER STATISTICAL ASSOC}},
Address = {{1429 DUKE ST, ALEXANDRIA, VA 22314}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{SAMPSON, PD (Reprint Author), UNIV WASHINGTON,DEPT STAT,SEATTLE,WA 98195, USA.}},
DOI = {{10.2307/2290458}},
ISSN = {{0162-1459}},
Research-Areas = {{Mathematics}},
Web-of-Science-Categories  = {{Statistics \& Probability}},
Number-of-Cited-References = {{50}},
Journal-ISO = {{J. Am. Stat. Assoc.}},
Doc-Delivery-Number = {{HF276}},
Unique-ID = {{ISI:A1992HF27600014}},
}



@Article{Savage2013,
AUTHOR = {Savage, N. H. and Agnew, P. and Davis, L. S. and Ord\'o\~nez, C. and Thorpe, R. and Johnson, C. E. and O'Connor, F. M. and Dalvi, M.},
TITLE = {Air quality modelling using the Met Office Unified Model (AQUM OS24-26): model description and initial evaluation},
JOURNAL = {Geoscientific Model Development},
VOLUME = {6},
YEAR = {2013},
NUMBER = {2},
PAGES = {353--372},
URL = {https://www.geosci-model-dev.net/6/353/2013/},
DOI = {10.5194/gmd-6-353-2013}
}


@Book{ChilesandDelfiner2012,
  author =       {Jean-Paul Chiles and Pierre Delfiner},
  title = 	 {Geostatistics},
  publisher = 	 {John Wiley and Sons},
  year =         {2012},
  address = 	 {Hoboken},
  edition = 	 {2nd},
}


@Book{banerjeebook2015,
  author =       {Banerjee, S. and Carlin, B. P. and Gelfand, A. E.},
  title = 	 {Hierarchical Modeling and Analysis for Spatial Data},
  publisher = 	 {CRC Press},
  year =         {2015},
  address = 	 {Boca Raton},
  edition = 	 {2nd},
}


@Book{banerjeebook2004,
  author =       {Banerjee, S. and Carlin, B. P. and Gelfand, A. E.},
  title = 	 {Hierarchical Modeling and Analysis for Spatial Data},
  publisher = 	 {CRC Press},
  year =         {2004},
  address = 	 {Boca Raton},
  edition = 	 {1st},
}


@Book{baddeley_et_al,
  author =       {Adrian Baddeley and  Ege Rubak and Rolf Turner},
  title = 	 {Spatial Point Patterns: Methodology and Applications with R},
  publisher = 	 {Chapman \& Hall/CRC},
  year =         {2015},
  address = 	 {Boca Raton},
  edition = 	 {1st},
}


@Book{digglebook,
  author =       {Peter Diggle},
  title = 	 {Statistical analysis of spatial point patterns},
  publisher = 	 {Chapman \& Hall/CRC},
  year =         {2014},
  address = 	 {Boca Raton},
  edition = 	 {3rd},
}

@Book{Berger1985,
  author =       {James O. Berger},
  title = 	 {Statistical Decision Theory and Bayesian Analysis},
  publisher = 	 {Springer-Verlag},
  year =         {1985},
  address = 	 {New York},
  edition = 	 {2nd},
}

@Book{shaddickandzidek,
  author =       {Gavin Shaddick and James V. Zidek},
  title = 	 {Spatio-Temporal Methods in Environmental Epidemiology},
  publisher = 	 {Chapman \& Hall/CRC},
  year =         {2015},
  address = 	 {Boca Raton},
  edition = 	 {1st},
}


@Book{cressieandwikle,
  author =       {Cressie, N A C and Wikle, C K},
  title = 	 {Statistics for Spatio-Temporal Data},
  publisher = 	 {John Wiley \& Sons},
  year =         {2011},
  address = 	 {New York},
}


@Book{wikleetal2019,
  author =       {Wikle, C K and  Zammit-Mangion, A. and Cressie, N.},
  title = 	 {Spatio-Temporal Statistics with R},
  publisher = 	 {Chapman \& Hall/CRC},
  year =         {2019},
  address = 	 {Boca Raton},
}


@Book{KanevskiandMaignan,
  author =       {Mikhail Kanevski and  Michel Maignan},
  title = 	 {Analysis and Modelling of Spatial Environmental Data},
  publisher = 	 {Chapman \& Hall/CRC},
  year =         {2004},
  address = 	 {Boca Raton},
}





@article{SahuandMukhop2015,
  title = {On generating a flexible class of anisotropic spatial models using Gaussian predictive processes},
  author = {Sujit K. Sahu and Sabyasachi Mukhopadhyay},
  year = {2015},
  journal = {Technical Report, University of Southampton},
  volume = {},
  number = {},
  pages = {},
  url = {https://www.southampton.ac.uk/~sks/research/}
}


@Article{spBayes,
  author = {A. O. Finley and S. Banerjee and A. E. Gelfand},
  title = { {\tt spBayes} for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models},
  journal = {Journal of Statistical Software},
  year = {2015},
  volume = {63},
  number = {13},
  pages = {1--28},
  url = {https://www.jstatsoft.org/v63/i13/}
}


@article{Camelettietal2013,
  title = {Spatio-temporal modeling of particulate matter concentration through the SPDE approach},
  author = {Michela Cameletti  and Finn Lindgren and  Daniel Simpson and H{\aa}vard Rue},
  year = {2013},
  journal = {Advances in Statistical Analysis},
  volume = {97},
  number = {2},
  pages = {109-131},

}


@Article{banetal2008,
  author =       {Banerjee, S. and Gelfand, A. E  and Finley, A. O and  Sang, H },
  title =        {Gaussian Predictive Process Models for Large Spatial Data Sets},
  journal =     {Journal of Royal Statistical Society, Series B},
  year =         {2008},
  volume =    {70},
  number =    {},
  month =     {},
  pages =     {825-848},
  note =      {},
  annote =    {}
}


@Article{Guhaniyogi2011,
  author = {Rajarshi Guhaniyogi and Andrew O. Finley and Sudipto Banerjee and Alan E. Gelfand},
  journal = {Environmetrics},
  number = {8},
  pages = {997--1007},
  title = {Adaptive Gaussian Predictive Process Models for Large Spatial Datasets},
  volume = {22},
  year = {2011},
}



@Article{Zimmerman1993,
  author = {Dale L. Zimmerman},
  journal = {Mathematical Geology},
  number = {4},
  pages = {453--470},
  title = {Another look at anisotropy in Geostatistics},
  volume = {25},
  year = {1993},
}





@article{finleySpBayes2007,
  author =       {Finley, A. O. and Banerjee, S. and  Carlin, B. P.},
  title =        {{\tt spBayes}: An {\tt R} Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models},
  journal =     {Journal of Statistical Software},
  year =         {2007},
  volume =    {12},
  number =    {4},
  month =     {},
  pages =     {1-24},
  note =      {},
  annote =    {}
}


@article{Katzfuss2013,
  author =       {Matthias Katzfuss},
  title =        {Bayesian nonstationary spatial modeling for very large datasets},
  journal =     {Environmetrics},
  year =         {2013},
  volume =    {24},
  number =    {3},
  month =     {},
  pages =     {189--200},
  note =      {10.1002/env.2200},
  annote =    {}
}


@article{Guhaniyogi2013,
  author =       {Guhaniyogi, Rajarshi and  Finley, Andrew O. and  Banerjee, Sudipto and Kobe, Richard K.},
  title =        {Modeling Complex Spatial Dependencies: Low-Rank Spatially Varying Cross-Covariances With Application to Soil Nutrient Data},
  journal =     {Journal of Agricultural Biological and Environmental Statistics},
  year =         {2013},
  volume =    {18},
  number =    {3},
  month =     {},
  pages =     {274--298},
  note =      {},
  annote =    {}
}


@article{Konomi2014,
  author =       {Konomi, Bledar A. and Sang, Huiyan and  Mallick, Bani K.},
  title =        {Adaptive Bayesian Nonstationary Modeling for Large Spatial Datasets Using Covariance Approximations},
  journal =     {Journal of Computational and Graphical Statistics},
  year =         {2014},
  volume =    {23},
  number =    {3},
  month =     {},
  pages =     {802--829},
  note =      {},
  annote =    {}
}


%Article end
@article{EckerandGelfand1999,
  author  = {Ecker, M.D. and Gelfand, A.E},
  title   = {Bayesian Modeling and Inference for Geometrically Anisotropic Spatial Data},
  volume  = {31},
  pages   = {67-83},
  year    = {1999},
  journal = {Mathematical Geology}
}

@article{SahuMardia2005,
  author  = {Sahu, S. K. and Mardia, K. V.},
  title   = {A Bayesian Kriged-Kalman model for short-term forecasting of air pollution levels},
  volume  = {54},
  pages   = {223-244},
  year    = {2005},
  journal = {Journal of the Royal Statistical Society, Series C}
}
@inproceedings{sahumardia2005b,
  author  = {Sahu, S. K. and Mardia, K. V.},
  title   = {Recent trends in modeling spatio-temporal data},
  publisher = {Universit\`{a} Di Messina},
  pages   = {69-83},
  year    = {2005},
  month = {September},
  booktitle = {Proceedings of the Special meeting on Statistics and Environment}
}






@article{bauer2001,
  author  = {Bauer, G. and  Deistler, M. and Scherrer, W. },
  title   = {Time series models for short term forecasting of ozone in the eastern part of Austria},
  volume  = {12},
  pages   = {117-130},
  year    = {2001},
  journal = {Environmetrics}
}


@article{BerrocalJabes,
  author  = {Berrocal, V. J. and  Gelfand, A. E. and Holland, D. M.},
  title   = {A spatio-temporal downcsaler for outputs from numerical models.},
  volume  = {15},
  pages   = {176-197},
  year    = {2010},
  journal = {Journal of Agricultural, Biological and Environmental Statistics}
}


@article{BerrocalAoas,
  author  = {Berrocal, V. J. and  Gelfand, A. E. and Holland, D. M.},
  title   = {A bivariate space-time downcsaler under space and time misalignment},
  volume  = {4},
  pages   = {1942-1975},
  year    = {2010},
  journal = {Annals of Applied Statistics}
}

@article{Cameletti2011,
  author  = {Cameletti, M. and  Ignaccolo, R. and Bande, S.},
  title   = {Comparing spatio-temporal models for particulate matter in Piemonte},
  volume  = {22},
  pages   = {985-996},
  year    = {2011},
  journal = {Environmetrics}
}

@techreport{Ching1999,
  author  = {Ching, J. and Byun D.},
  title   = {Science algorithms of the EPA models-3 community multi-scale air quality (CMAQ) modeling system},
  institution = {National  Exposure Research Laboratory, Research Triangle Park, NC, USA},
  number = {EPA/ 600/R-99/030},
  year    = {1999},
}


@article{cressiejohan2008,
  author =    {Cressie, N. A. C. and Johannesson, G},
  title =        {Fixed rank kriging for very large spatial data sets},
  journal =    {Journal of the Royal Statistical Society, Series: B},
  year =         {2008},
  volume =    {70},
  pages =     {209-226}
}

@article{DamonGuillas2002,
  author  = {Damon, J. and Guillas, S.},
  title   = {The inclusion of exogenous variables in functional autoregressive ozone forecasting. },
  volume  = {13},
  pages   = {759-774.},
  year    = {2002},
  journal = {Environmetrics}
}

@article{FeisterBalzer1991,
  author  = {Feister, U. and Balzer, K.},
  title   = {Surface ozone and meteorological predictors on a subregional scale},
  volume  = {25},
  pages   = {1781-1790},
  year    = {1991},
  journal = {Atmospheric Environment}
}




@article{fuentesandraftery,
  author  = {Fuentes, M. and Raftery, A.},
  title   = {Model evaluation and spatial interpolation by Bayesian combination of observations with outputs from numerical models.},
  volume  = {61},
  pages   = {36-45},
  year    = {2005},
  journal = {Biometrics}
}




@article{Gneitingetal2007,
  author  = {Gneiting, T. and  Balabdaoui, F. and Raftery, A.E. },
  title   = {Probabilistic forecasts, calibration and sharpness},
  volume  = {69},
  pages   = {243-268.},
  year    = {2007},
  journal = {Journal of the Royal Statistical Society, Series B}
}


@article{Huertaetal2004,
  author  = {Huerta, G. and  Sanso, B. and Stroud, J. R.},
  title   = {A Spatio-Temporal Model for Maxico City Ozone Levels},
  volume  = {53},
  pages   = {231-248},
  year    = {2004},
  journal = {Journal of the Royal Statistical Society, Series C}
}

@article{JunStein,
  author  = {Jun, M. and Stein, M. L.},
  title   = {Statistical Comparison of Observed and CMAQ Modeled Daily Sulfate Levels},
  volume  = {38},
  pages   = {4427-4436.},
  year    = {2004},
  journal = {Atmospheric Environment}}
}

@article{Kumar2010,
  author  = {Kumar, U. and Ridder, K. D.},
  title   = {GARCH modelling in association with FFT-ARIMA to forecast ozone episodes},
  volume  = {44},
  pages   = {4252-4265},
  year    = {2010},
  journal = {Atmospheric Environment}
}

@article{Lorence,
  author  = {Lorence, A. C. },
  title   = { Analysis methods for numerical weather prediction},
  volume  = {112},
  pages   = {1177-1194},
  year    = {1986},
  journal = {The Quarterly Journal of the Royal Meteorological Society}
}

@article{Mardia98,
  author  = {Mardia, K. V. and  Goodall, C. and  Redfern, E. J. and Alonso, F.J. },
  title   = {The Kriged Kalman filter (with discussion)},
  volume  = {7},
  pages   = {217-252},
  year    = {1998},
  journal = {Test}
}

@article{McMillan2005,
  author  = {McMillan, N. and Bortnick, S. M. and  Irwin, M. E. and Berliner, M.},
  title   = {A Hierarchical Bayesian Model to Estimate and Forecast Ozone Through Space and Time},
  volume  = {39},
  pages   = {1373-1382.},
  year    = {2005},
  journal = {Atmospheric Environment}
}

@Book{Petris,
  author  = {Petris, G. and Petrone, S. and Patrizia, C. },
  title   = {Dynamic Linear Models with R},
  address ={Dordrecht},
  year    = {2010},
  publisher= {Springer}
}



@ARTICLE{coda,
  AUTHOR = {Plummer, M.  and Best, N. and  Cowles K.  and Vines, K},
  TITLE = {CODA: Convergence Diagnosis and Output Analysis for {MCMC}},
  JOURNAL = {R News},
  YEAR = 2006,
  VOLUME = 6,
  NUMBER = 1,
  PAGES = {7--11},
  MONTH = {March},
  URL = {https://CRAN.R-project.org/doc/Rnews/},
  PDF = {https://CRAN.R-project.org/doc/Rnews/Rnews_2006-1.pdf}
}

@article{SyhAE,
  author =       {Sahu, S. K. and Yip, S. and Holland, D. M.},
  title =        {Improved space-time forecasting of next day ozone concentrations in the eastern U.S.},
  journal =     "Atmospheric Environment",
  year =         {2009},
  volume =    {43},
  number =    {},
  month =     {},
  pages =     {494-501},
  note =      {},
  annote =    {}
}


@article{SyhEES2011,
  author  = {Sahu, S. K. and  Yip, S. and Holland, D. M.},
  title   = {A fast Bayesian method for updating and forecasting hourly ozone levels},
  volume  = {18},
  pages   = {185-207},
  year    = {2011},
  journal = {Environmental and Ecological Statistics}
}

@inproceedings{Akaike1973,
  author  = {Akaike, H. },
  title   = {Information theory and an extension of the maximum likelihood principle},
  booktitle = {2nd International Symposium on Information Theory},
  pages   = {267-281},
  year    = {1973},
  editor = {Petrov, B. N. and  Cs\'{a}ki, F.},
  publisher = {Budapest: Akad\'{e}miai Kiad\'{o}}
}


@incollection{Raftery1996,
  author  = {Raftery, A. E.},
  title   = {Hypothesis testing and  model selection},
  booktitle={Markov Chain Monte Carlo in Practice},
  editor = {Gilks, Walter R and Richardson, Sylvia and Spiegelhalter, David J},
  pages   = {163-187},
  year    = {1996},
  publisher = {CRC Press}
}

@incollection{Roberts1996,
  author  = {Roberts, G. O.},
  title   = {Markov chain concepts related to sampling algorithms},
  booktitle={Markov Chain Monte Carlo in Practice},
  editor = {Gilks, Walter R and Richardson, Sylvia and Spiegelhalter, David J},
  pages   = {45-57},
  year    = {1996},
  publisher = {CRC Press}
}


@incollection{Tierney1996,
  author  = {Luke Tierney},
  title   = {Introduction to general state-space Markov chain theory},
  booktitle={Markov Chain Monte Carlo in Practice},
  editor = {Gilks, Walter R and Richardson, Sylvia and Spiegelhalter, David J},
  pages   = {59-74},
  year    = {1996},
  publisher = {CRC Press}
}

@incollection{Sahu2012,
  author  = {Sahu, S.K.},
  title   = {Hierarchical Bayesian models for space-time air pollution data},
  booktitle={Handbook of Statistics-Vol 30.  Time Series Analysis: Methods and Applications},
  editor = {T Subba Rao, S Subba Rao  and C R Rao},
  pages   = {477-495},
  year    = {2012},
  publisher = {Elsevier}
}



@article{sahubakarasmbi,
  author =   {Sahu, Sujit K. and Bakar, K. S},
  title =        {Hierarchical Bayesian auto-regressive models for large space time data with applications to ozone concentration modelling},
  journal =     "Applied Stochastic Models in Business and Industry",
  year =         {2012},
  volume =    {28},
  number =    {},
  month =     {},
  pages =     {395-415},
  note =      {},
  annote =    {}
}


@article{stein2008,
  author =    {Stein, M. L.},
  title =        {A modelling approach for large spatial datasets},
  journal =     "Journal of the Korian Statistical Society",
  year =         {2008},
  volume =    {37},
  number =    {},
  month =     {},
  pages =     {3-10},
  note =      {},
  annote =    {}
}


@article{Sousa,
  author  = {Sousa, S. I. V.  and Pires, J. C. M. and Martins, F.G. and  Pereira, M. C.  and Alvim-Ferraz, M. C. M.},
  title   = {Potentialities of quantile regression to predict ozone concentrations},
    year    = {2009},
     volume  = {20},
  pages   = {147-158},
    journal = {Environmetrics}
}

@article{Stroud,
  author  = {Stroud, J. R. and Muller, P. and Sanso, B.},
  title   = {Dynamic Models for Spatio-Temporal Data},
  volume  = {63},
  pages   = {673-689},
  year    = {2001},
  journal = {Journal of the Royal Statistical Society, Series B}
}

@Book{WestandHarrison,
  author =       {West, M. and Harrison, J.},
  title = 	 {Bayesian Forecasting and Dynamic Models},
  publisher = 	 {Springer},
  year =         {1997},
  address = 	 {New York},
  edition = 	 {2nd},
}


@article{Zidek2012,
  author  = {Zidek, J. V. and  Le, N. D. and Liu, Z. },
  title   = {Combining data and simulated data for space-time fields: application to ozone.},
  volume  = {19},
  pages   = {37-56},
  year    = {2012},
  journal = {Environmental and Ecological Statistics}
}



@article {crimp2014bayesian,
author = {Crimp, Steven and Bakar, Khandoker Shuvo and Kokic, Philip and Jin, Huidong and Nicholls, Neville and Howden, Mark},
title = {Bayesian Space-Time Model to Analyse Frost Risk for Agriculture in Southeast Australia},
journal = {International Journal of Climatology},
publisher = {John Wiley & Sons, Ltd},
issn = {1097-0088},
url = {https://dx.doi.org/10.1002/joc.4109},
doi = {10.1002/joc.4109},
year = {2014},
}


@Book{anderson,
  author =    {Anderson, E and Bai, Z and Bischof, C and Blackford, S and Demmel, J and Dongarra, J and Du Croz, J and Greenbaum, A and Hammarling, S and McKenney, A and Sorensen, D},
  title = 	 {{\tt LAPACK} Users Guide},
  publisher = 	 {Society for Industrial and Applied Mathematics},
  year =         {1999},
  address = 	 { Philadelphia, PA},
  edition = 	 {Third},
}



@Article{ban,
  author =       {Banerjee, Sudipto and Gelfand, Alan E  and Finley, Andrew O and  Sang, H },
  title =        {Gaussian Predictive Process Models for Large Spatial Data Sets},
  journal =     {Journal of the Royal Statistical Society B},
  year =         {2008},
  volume =    {70},
  pages =     {825-848},
}



@Book{Jeffreys_1961,
  author =       {H. Jeffreys},
  title = 	 {Theory of Probability},
  publisher = 	 {Clarendon Press},
  year =         {1961},
  address = 	 {Oxford},
  edition={3rd}
}


@Article{black,
  author =   {Blackford, S L and Demmel, J and Dongarra, J and Duff, I and Hammarling, S and Henry, G and Heroux, M and Kaufman, L and Lumsdaine, A and Petitet, A and Pozo, R and Remington, K and Whaley, C R},
  title =        {An Updated Set of Basic Linear Algebra Subprograms ({\tt BLAS})},
  journal =     {Transactions on Mathematical Software},
  year =         {2002},
  volume =    {28},
  number =    {2},
  pages =     {135-151},
}



@article{cameletti,
  author =    {Cameletti, M and Ignaccolo, R and Bande, S},
  title =        {Comparing Air Quality Statistical Models},
  journal =     {Technical Report},
  year =         {2009},
  note =      {University of Bergamo, Italy.},
}


@Book{cressie,
  author =       {Cressie, N A C},
  title = 	 {Statistics for Spatial Data},
  publisher = 	 {John Wiley \& Sons},
  year =         {1993},
  address = 	 {New York},
}



@article{cressie2,
  author =    {Cressie, N A C and Johannesson, G},
  title =        {Fixed Rank Kriging for Very Large Spatial Data Sets},
  journal =     {Journal of the Royal Statistical Society B},
  year =         {2008},
  volume =    {70},
  pages =     {209-226},
}


@Book{diggle,
  author =     {Diggle, PJ and Ribeiro, PJ Jnr},
  title = 	 {Model-based Geostatistics},
  publisher = 	 {Springer-Verlag},
  year =         {2007},
  address = 	 {New York},
  url = {https://asdar-book.org/},
}

@article{finley,
  author =    {Finley, Andrew O and Banerjee, Sudipto and  Carlin, Bradley P},
  title =        {{\tt spBayes}: An {\tt R} Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models},
  journal =     {Journal of Statistical Software},
  year =         {2007},
  volume =    {12},
  number =    {4},
  pages =     {1-24},
}

@article{finley2,
  author =       {Finley, Andrew O and Sang, H and Banerjee, Sudipto and Gelfand, A E},
  title =        {Improving the Performance of Predictive Process Modeling for Large Datasets},
  journal =     {Computational Statistics and Data Analysis},
  year =         {2009},
  volume =    {53},
  pages =     {2873-2884},
}


@article{geman,
  author =       {Geman, S and Geman, D},
  title =        {Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images},
  journal =     {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year =         {1984},
  volume =    {6},
  number =    {6},
  pages =     {721-741},
}

@article{gelfand2012,
  author =       {Gelfand, Alan E},
  title =        {Hierarchical Modeling for Spatial Data Problems},
  journal =     {Spatial Statistics},
  year =         {2012},
  volume =    {1},
  pages =     {30-39},
}

@Book{gelfand2010,
  author =     {Gelfand, A E and  Diggle, P J and  Fuentes, M and Guttorp, P},
  title = 	 {The Handbook of Spatial Statistics},
  publisher = 	 {Chapman \& Hall/CRC},
  year =         {2010},
  address = 	 {New York},
}

@article{GelfandSahuHolland2012,
  author =       {Gelfand, A. E. and Sahu, S. K. and Holland, D. M.},
  title =        {On the Effect of Preferential Sampling in Spatial Prediction},
  journal =     {Environmetrics},
  year =         {2012},
  volume =    {23},
  pages =     {565-578},
}

%Gelfand, A. E., Sahu S. K. and Holland, D. M. (2012) On the Effect of Preferential Sampling in Spatial Prediction. Environmetrics, 23, 565-578.

@incollection{Gelmanetal1996,
  author  = {Gelman, A. and Roberts, G. O. and Gilks, W. R.},
  title   = {Efficient Metropolis Jumping Rules},
  booktitle={Bayesian Statistics 5},
  editor = { J. M. Bernardo and  J. O. Berger and A. P. Dawid and A. F. M. Smith.},
  pages   = {599-607},
  publisher = {Oxford University Press},
  year    = {1996},
}



@incollection{GelfandSahu2009,
  author  = {Gelfand, A. E. and Sahu, S. K},
  title   = {Monitoring Data and Computer model Output in Assessing Environmental Exposure.},
  booktitle={Handbook of Applied Bayesian Analysis},
  editor = {O'Hagan, A. and West, M.},
  pages   = {482-510},
  publisher = {Oxford University Press},
  year    = {2009},
}


@article{gelman92,
  author =       {Gelman, A and Rubin, D B},
  title =        {Inference from iterative simulation using multiple sequences},
  journal =     {Statistical Science},
  year =         {1992},
  volume =    {7},
  number =    {4},
  pages =     {457-472},
}

@Book{gelman2004,
  author =       {Gelman, Andrew and Carlin, John B and Stern, Hal S and Rubin, Donald B},
  title = 	 {Bayesian Data Analysis},
  publisher = 	 {Chapman \& Hall/CRC},
  year =         {2004},
  address = 	 {Boca Raton},
  edition = 	 {2nd},
}


@article{hand,
  author =       {Handcock, M S and Stein, M L},
  title =        {A Bayesian Analysis of Kriging},
  journal =     {Technometrics},
  year =         {1993},
  volume =    {35},
  pages =     {403-410},
}

@article{hand2,
  author =       {Handcock, M S and Wallis, J},
  title =        {An Approach to Statistical Spatial-Temporal Modelling of Meteorological Fields},
  journal =     {Journal of the American Statistical Association},
  year =         {1994},
  volume =    {89},
  pages =     {368-390},
}

@Book{hastieGAM90,
  author =       {Hastie, Trevor and Tibshirani, Robert },
  title = 	 {Generalized Additive Models},
  publisher = 	 {Chapman \& Hall},
  year =         {1990},
  address = 	 {London},
}


@article{johannesson,
  author =   {Johannesson, G and Cressie, N},
  title =        {Finding Large-Scale Spatial Trends in Massive, Global, Environmental Datasets},
  journal =     {Environmetrics},
  year =         {2004},
  volume =    {15},
  pages =     {1-44},
}


@inbook{kent,
  author =	 {Kent, J T K and Mardia, K V},
  title =	  {Modelling Strategies for Spatial-Temporal Data},
  chapter =	 {11},
  pages =	 {214-226},
  address =	 {London},
  publisher =	 {Eds. A Lawson and D Denison, Chapman and Hall},
  year =	 {2002},
}

@inbook{mardiaandgoodall,
  author =	 {Mardia, K V and Goodall, C.},
  chapter =	  {Spatial-temporal analysis of multivariate environmental monitoring data},
  title =	 {Multivariate Environmental Statistics},
  pages =	 {347-386},
  address =	 {Amsterdam: Elsevier},
  publisher =	 {Eds.  G. P. Patil and C. R. Rao},
  year =	 {1993},
}



@article{krige51,
  author =       {Krige, D G},
  title =        {A Statistical Approach to Some Basic Mine Valuation Problems on the Witwatersrand},
  journal =     {Journal of the Chemical, Metallurgical and Mining Society of South Africa},
  year =         {1951},
  volume =    {52},
  pages =     {119-139},
}



@inbook{lasinio,
  author =	 {Lasinio, G Jona and Sahu, S K and Mardia, K V},
  title =	  {Bayesian Statistics and its Applications},
  address =	 {London},
  chapter = {1},
  publisher =	 {Eds. S K Upadhyay, U Singh and D K Dey, Anshan Ltd.},
  year =	 {2007},
}


@article{geyer92,
  author =       {Geyer, C. J.},
  title =        {Practical Markov chain Monte Carlo},
  journal =     {Statistical Science},
  year =         {1992},
  volume =    {7},
  pages =     {473-483},
}



@article{ribeiro,
  author =       {Ribeiro, Paulo J and Diggle, Peter J},
  title =        {{\tt geoR}: Analysis of Geostatistical Data},
  journal =     {{\tt R} News},
  year =         {2001},
  volume =    {1},
  number =    {2},
  pages =     {14-18},
}


@Book{rue,
  author =   {Rue, H and Held, L},
  title =  {Gaussian Markov Random Fields: Theory and Applications},
  publisher = 	 {Chapman and Hall/CRC},
  year =         {2006},
  address = 	 {Boca Raton},
}


@Book{shekhar,
  author =       {Shekhar, S and Xiong, H},
  title = 	 {Encyclopedia of GIS},
  publisher = 	 {Springer-Verlag},
  year =         {2008},
  address = 	 {New York},
  url = {https://asdar-book.org/},
}



@article{sahuetal2010,
  author =       {Sahu, Sujit K and Gelfand, Alan E and Holland, David M},
  title =        {Fusing Point and Areal Level Space-Time Data with Application to Wet Deposition},
  journal =     {Journal of the Royal Statistical Society C},
  year =         {2010},
  volume =    {59},
  pages =     {77-103},
}



@incollection{sahubakarandawang,
  author = {Sahu, Sujit K. and Bakar, Khandoker S. and Awang, N.},
  title =  {Bayesian Forecasting Using Hierarchical Spatio-temporal Models with Applications to Ozone Levels in the Eastern United States},
  booktitle={Geometry Driven Statistics},
  year =    {2015},
  pages =   {260-281},
  editor = {I. L. Dryden and J. Kent},
 publisher = {John Wiley and Sons: Chichester},
}



@article{sahu2006,
  author =       {Sahu, Sujit K and Gelfand, Alan E and Holland, David M},
  title =        { Spatio-Temporal Modeling of Fine Particulate Matter},
  journal =     {Journal of Agricultural, Biological, and Environmental Statistics},
  year =         {2006},
  volume =    {11},
  pages =     {61-86},
}


@article{stein,
  author =    {Stein, M L},
  title =        {A Modelling Approach for Large Spatial Datasets},
  journal =     {Journal of the Korian Statistical Society},
  year =         {2008},
  volume =    {37},
  pages =     {3-10},
}



@article{winbugs1,
  author =    {Lunn, D J and Thomas, A and Best, N and Spiegelhalter, D},
  title =        {WinBUGS - a Bayesian modelling framework: concepts, structure, and extensibility},
  journal =     {Statistics and Computing},
  year =         {2000},
  volume =    {10},
  pages =     {325-337},
}

@book{bugsbook,
  author =    {Lunn, D J and Thomas, A and Best, N and Spiegelhalter, D},
  title =     {The BUGS Book: A Practical Introduction to Bayesian Analysis},
  publisher ={Chapman \& Hall},
  year =         {2013}
}

@Manual{winbugs2,
  title  = {WinBUGS Version 1.2 User Manual},
  author = {Spiegelhalter, D J and Thomas, A and Best, N G},
  year   = {1999},
  note   = {MRC Biostatistics Unit},
  url    = {https://www.mrc-bsu.cam.ac.uk/bugs},
}


%%%%%%%% R packages


@Article{sp1,
    author = {Edzer J. Pebesma and Roger S. Bivand},
    title = {Classes and Methods for Spatial Data in {\tt R}},
    journal = {R News},
    year = {2005},
    volume = {5},
    number = {2},
    pages = {9--13},
    month = {November},
  }



@Article{gstat1,
    title = {Multivariable Geostatistics in {\tt S}: The {\tt gstat} Package},
    author = {Edzer J. Pebesma},
    journal = {Computers and Geosciences},
    year = {2004},
    volume = {30},
    pages = {683-691},
  }

%Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 1, 515–533


@Manual{stan,
    title = {Stan modeling language: Users guide and reference manual},
    author = {{Stan Development Team}},
    organization = {Columbia University},
    address = {Columbia, New York},
    year = {2015},
    url =  {https://mc-stan.org/documentation},
}


@Manual{rprog,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2016},
    url = {https://www.R-project.org/},
}

@Manual{geoR,
  author =   {Ribeiro, Paulo J and Diggle, Peter J},
  title =       {{\tt geoR}: Analysis of Geostatistical Data},
  year   = {2012},
  note   = {{\tt R}~package version~1.7-4},
  url    = {https://cran.r-project.org/web/packages/geoR},
}

@Article{geoR1,
    title = {{\tt geoR}: A Package for Geostatistical Analysis},
    author = {Ribeiro, P J and Diggle, P J},
    journal = {R-NEWS},
    year = {2001},
    month = {June},
    volume = {1},
    number = {2},
    pages = {14--18},
    note = {ISSN 1609-3631},
  }

@Manual{geoRglm,
  author =   {Christensen, Ole F  and Ribeiro, Paulo J},
  title =   {{\tt geoRglm}: A Package for Generalised Linear Spatial Models},
  year   = {2011},
  note   = {{\tt R}~package version~0.9-2},
  url    = {https://cran.r-project.org/web/packages/geoRglm},
}


@Manual{fields1,
  title  = {fields: Tools for Spatial Data},
  author = {Furrer, Reinhard and Nychka, Douglas and Sain, Stephen},
  year   = {2013},
  note   = {R~package version~8.4.1},
  organization = {University Corporation for Atmospheric Research},
  url    = {https://cran.r-project.org/web/packages/fields},
}

@Manual{fields19999,
  title  = {{\tt fields}: Tools for Spatial Data},
  author = {Furrer, Reinhard and Nychka, Douglas and Sain, Stephen},
  year   = {2013},
  note   = {{\tt R}~package version~6.9.1},
  organization={University Corporation for Atmospheric Research},
  url    = {https://cran.r-project.org/web/packages/fields},
}

@Manual{sp,
  author =   {Pebesma, E and Bivand, B and Rowlingson, B and Rubio, V Gomez},
  title =       {{\tt sp}: Classes and Methods for Spatial Data},
  year   = {2012},
  note   = {{\tt R}~package version~1.0-5},
  url    = {https://cran.r-project.org/web/packages/sp},
}

@Manual{spacetime,
  author =   {Pebesma, E and Graeler, B and Gottfried, T and Hijmans, RJ},
  title =       {{\tt spacetime}: Classes and Methods for Spatial Data},
  year   = {2012},
  note   = {{\tt R}~package version~1.0-3},
  url    = {https://cran.r-project.org/web/packages/spacetime},
}

@Book{rogerbook13,
    author = {Roger S. Bivand and Edzer Pebesma and Virgilio
      Gomez-Rubio},
    title = {Applied Spatial Data Analysis with {R}, Second edition},
    year = {2013},
    publisher = {Springer-Verlag},
    address = {New York},
    url = {https://asdar-book.org/},
  }

@Manual{gstat,
  author =   {Pebesma, Edzer},
  title =       {{\tt gstat}: Spatial and Spatio-temporal Geostatistical Modelling, Prediction and Simulation},
  year   = {2013},
  note   = {{\tt R}~package version~1.0-15},
  url    = {https://cran.r-project.org/web/packages/gstat},
}

@Manual{splm,
  author =   {Millo, Giovanni and Piras Gianfranco},
  title =       {{\tt splm}: Econometric Models for Spatial Panel Data},
  year   = {2012},
  note   = {{\tt R}~package version~1.0-00},
  url    = {https://cran.r-project.org/web/packages/splm},
}

@Manual{spatial,
  author =   {Ripley, Brian},
  title =       {{\tt spatial}: Functions for Kriging and Point Pattern Analysis},
  year   = {2013},
  note   = {{\tt R}~package version~7.3-7},
  url    = {https://cran.r-project.org/web/packages/spatial},
}

@Book{spatial1,
    title = {Modern Applied Statistics with {\tt S}},
    author = {W. N. Venables and B. D. Ripley},
    publisher = {Springer-Verlag},
    edition = {Fourth},
    address = {New York},
    url = {https://asdar-book.org/},
    year = {2002},
  }

@Manual{spatstat,
  author =   {Baddeley, Adrian and Turner, Rolf},
  title =       {{\tt spatstat}: Spatial Point Pattern Analysis, Model-fitting, Simulation, Tests},
  year   = {2013},
  note   = {{\tt R}~package version~1.34-1},
  url    = {https://cran.r-project.org/web/packages/spatstat},
}

@Article{spatstat1,
    title = {{\tt spatstat}: An {\tt R} Package for Analyzing Spatial Point
      Patterns},
    author = {Adrian Baddeley and Rolf Turner},
    journal = {Journal of Statistical Software},
    year = {2005},
    volume = {12},
    number = {6},
    pages = {1--42},
 }

@Article{spatstat2,
    title = {Hybrids of Gibbs Point Process Models and Their Implementation},
    author = {Adrian Baddeley and Rolf Turner and Jorge Mateu and  Andrew Bevan},
    journal = {Journal of Statistical Software},
    year = {2013},
    volume = {55},
    number = {11},
    pages = {1--43},
  }


@Manual{mcmcpack,
  author =   {Martin, AD and Quinn, KM and Park, JH},
  title =       {{\tt MCMCpack}: Markov chain Monte Carlo (MCMC) Package},
  year   = {2012},
  note   = {{\tt R}~package version~1.2-4},
  url    = {https://cran.r-project.org/web/packages/MCMCpack},
}
@Article{mcmcpack1,
    title = {{\tt MCMCpack}: Markov Chain Monte Carlo in {\tt R}},
    author = {Andrew D. Martin and Kevin M. Quinn and Jong Hee Park},
    journal = {Journal of Statistical Software},
    year = {2011},
    volume = {42},
    number = {9},
    pages = {22},
  }

@Manual{mcmcglmm,
  author =   {Hadfield, Jarrod},
  title =       {{\tt MCMCglmm}: MCMC Generalised Linear Mixed Models},
  year   = {2012},
  note   = {{\tt R}~package version~2.17},
  url    = {https://cran.r-project.org/web/packages/MCMCglmm},
}
@Article{mcmcglmm1,
    title = {MCMC Methods for Multi-Response Generalized Linear Mixed
      Models: The { \tt MCMCglmm} {\tt R} Package},
    author = {Jarrod D Hadfield},
    journal = {Journal of Statistical Software},
    year = {2010},
    volume = {33},
    number = {2},
    pages = {1--22},
  }

@Manual{blr,
  author =   {Campos, Gdelos and Rodriguez, PP},
  title =       {{\tt BLR}: Bayesian Linear Regression},
  year   = {2012},
  note   = {{\tt R}~package version~1.3},
  url    = {https://cran.r-project.org/web/packages/BLR},
}

@Manual{gam,
  author =   {Hastie, Trevor},
  title =       {{\tt gam}: Generalized Additive Models},
  year   = {2013},
  note   = {{\tt R}~package version~1.09},
  url    = {https://cran.r-project.org/web/packages/gam},
}

@Manual{mgcv,
  author =   {Wood, Simon N},
  title =       {{\tt mgcv}: Mixed GAM Computation Vehicle with GCV / AIC / REML Smoothness Estimation},
  year   = {2013},
  note   = {{\tt R}~package version~1.7-27},
  url    = {https://cran.r-project.org/web/packages/mgcv},
}

@Manual{r2winbugs,
  author =   {Liggrs,Uwe},
  title =       {{\tt R2WinBUGS}: Running WinBUGS and OpenBUGS from R / S-PLUS},
  year   = {2011},
  note   = {{\tt R}~package version~2.1-18},
  url    = {https://cran.r-project.org/web/packages/R2WinBUGS},
}
@Article{r2winbugs1,
    title = {{\tt R2WinBUGS}: A Package for Running WinBUGS from {\tt R}},
    author = {Sibylle Sturtz and Uwe Ligges and Andrew Gelman},
    journal = {Journal of Statistical Software},
    year = {2005},
    pages = {1--16},
    number = {3},
    volume = {12},
  }

@Manual{rjags,
  author =   {Plummer, Martyn},
  title =       {{\tt rjags}: Bayesian Graphical Models using MCMC},
  year   = {2014},
  note   = {{\tt R}~package version~3-12},
  url    = {https://cran.r-project.org/web/packages/rjags},
}

@Manual{nlme,
  author =   {Pinheiro, Jose and Bates, Douglas and DebRoy, Saikat and Sarkar, Deepayan},
  title =       {{\tt nlme}: Linear and Nonlinear Mixed Effects Models},
  year   = {2013},
  note   = {{\tt R}~package version~3.1-113},
  url    = {https://cran.r-project.org/web/packages/nlme},
}

@Manual{akima,
  author =   {Akima, R},
  title =       {{\tt akima}: Interpolation of Irregularly Space Data},
  year   = {2013},
  note   = {{\tt R}~package version~0.5-11},
  url    = {https://cran.r-project.org/web/packages/akima},
}


@Manual{MKL,
  author = {{Intel}},
  title = {Intel {M}ath {K}ernel {Library} -- Documentation},
  year = {2013},
  url = {https://software.intel.com/en-us/articles/intel-math-kernel-library-documentation},
}

@Article{openbugs,
  author = {Andrew Thomas and Bob {O Hara} and Uwe Ligges and Sibylle Sturtz},
  title = {Making {{\tt BUGS}} Open},
  journal = {{\tt R} News},
  volume = {6},
  pages = {12-17},
  year = {2006},
  url = {https://CRAN.R-project.org/doc/Rnews/},
}

@Manual{MBA,
  title = {{\tt MBA}: Multilevel B-Spline Approximation},
  author = {Andrew O. Finley and Sudipto Banerjee},
  year = {2010},
  note = {{\tt R}~package version~0.0-7},
  url = {https://CRAN.R-project.org/package=MBA},
}

@Manual{fields,
  title = {{\tt fields}: Tools for Spatial Data},
  author = {Douglas Nychka and Reinhard Furrer and Stephan Sain},
  year = {2013},
  note = {{\tt R}~package version~6.9.1},
  url = {https://CRAN.R-project.org/package=fields},
}

@Manual{magic,
  title = {{\tt magic}: Create and Investigate Magic Squares},
  author = {Robin K. S. Hankin},
  year = {2013},
  note = {{\tt R}~package version~1.5-4},
  url = {https://CRAN.R-project.org/package=magic},
}

@Manual{abind,
  title = {{\tt abind}: Combine Multi-Dimensional Arrays},
  author = {Tony Plate and Richard Heiberger},
  year = {2013},
  note = {{\tt R}~package version~1.4-0},
  url = {https://CRAN.R-project.org/package=abind},
}

@Article{Formula,
  author	= {Achim Zeileis and Yves Croissant},
  title		= {Extended Model Formulas in {\tt R}: Multiple Parts and Multiple Responses},
  journal	= {Journal of Statistical Software},
  year		= {2010},
  volume	= {34},
  number	= {1},
  pages		= {1--13},
  url		= {https://www.jstatsoft.org/v34/i01/}
}

@Article{geoR,
  author = {P. J. Ribeiro and P. J. Diggle},
  title = {{\tt geoR}: A Package For Geostatistical Analysis},
  journal = {{\tt R} News},
  year = {2001},
  volume = {1},
  number = {2},
  pages = {15--18},
  url = {https://CRAN.R-project.org/doc/Rnews/},
}



@article{spate,
  title = {{\tt spate}: An {\tt R} Package for Spatio-Temporal Modeling with a Stochastic Advection-Diffusion Process},
  author = {Fabio Sigrist and Hans R. Kuensch and Werner A. Stahel},
  year = {2015},
  journal = {Journal of Statistical Software},
  volume = {63},
  number = {14},
  pages = {1--23},
  url = {https://www.jstatsoft.org/v63/i14/}
}


@Article{ramps,
  author	= {Brian J. Smith and Jun Yan and Mary Kathryn Cowles},
  title		= {Unified Geostatistical Modeling for Data Fusion and Spatial Heteroskedasticity with {\tt R} Package {\tt ramps}},
  journal	= {Journal of Statistical Software},
  year		= {2008},
  volume	= {25},
  number	= {10},
  pages		= {1--21},
  url		= {https://www.jstatsoft.org/v25/i10/}
}

@Manual{R,
  title = {{\tt R}: A Language and Environment for Statistical Computing},
  author = {{{\tt R} Core Team}},
  organization = {{\tt R} Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2014},
  url = {https://www.R-project.org},
}

@Manual{spTV,
  author = {Roger Bivand},
  title = {CRAN Task View: Analysis of Spatial Data},
  year = {2014},
  note = {Version~2014-12-18},
  url = {https://CRAN.R-project.org/view=Spatial},
}

@Manual{sptimeTV,
  author = {Edzer Pebesma},
  title = {CRAN Task View: Handling and Analyzing Spatio-Temporal Data},
  year = {2014},
  note = {Version~2014-12-18},
  url = {https://CRAN.R-project.org/view=SpatioTemporal},
}

@Book{anderson1999,
  author = {E. Anderson and Z. Bai and C. Bischof and S. Blackford and J. Demmel and J. Dongarra and J. Du Croz and A. Greenbaum and S. Hammarling and A. McKenney and D. Sorensen},
  title = {{\tt LAPACK} Users' Guide},
  edition = {3rd},
  publisher = {Society for Industrial and Applied Mathematics},
  year = {1999},
  addres = {Philadelphia},
}

@Book{banerjee2004,
  title = {{Hierarchical Modeling and Analysis for Spatial Data}},
  year = {2004},
  pages = {472},
  publisher = {Chapman \& Hall/CRC},
  address = {Boca Raton},
  author = {S. Banerjee and C.~P. Carlin and A.~E. Gelfand},
}

@Article{banerjee2008,
  title = {Gaussian Predictive Process Models for Large Spatial Data Sets},
  author = {Sudipto Banerjee and Alan E. Gelfand and Andrew O. Finley and Huiyan Sang},
  year = {2008},
  journal = {Journal of the Royal Statistical Society B},
  volume = {70},
  number = {4},
  pages = {825-848},
}

@Article{blackford2001,
  author = {L. S. Blackford and J. Demmel and J. Dongarra and I. Duff and S. Hammarling and G. Henry and M. Heroux and L. Kaufman and A. Lumsdaine and A. Petitet and R. Pozo and K. Remington and R. C. Whaley},
  title = {An Updated Set of Basic Linear Algebra Subprograms ({\tt BLAS})},
  journal = {ACM Transactions on Mathematical Software},
  year = {2001},
  volume = {28},
  pages = {135--151},
}

@Book{bochner1955,
  title = {Harmonic Analysis and the Theory of Probability},
  author = {S. Bochner},
  series = {California Monographs in mathematical sciences},
  year = {1955},
  publisher = {University of California Press},
}

@Book{carlin2011,
  title = {Bayesian Methods for Data Analysis},
  edition = {3rd},
  author = {B. P. Carlin and T. A. Louis},
  year = {2011},
  publisher = {Taylor \& Francis},
}

@Book{chil2012,
  title = {Geostatistics: Modeling Spatial Uncertainty},
  author = {J. P. Chil\`es and P. Delfiner},
  year = {2012},
  publisher = {John Wiley \& Sons},
}

@Book{cressie1993,
  title = {Statistics for Spatial Data},
  author = {N. A. C. Cressie},
  edition = {2nd},
  year = {1993},
  publisher = {John Wiley \& Sons},
}

@Book{cressie2011,
  title = {Statistics for Spatio-Temporal Data},
  author = {N. Cressie and C. K. Wikle},
  year = {2011},
  publisher = {John Wiley \& Sons},
}

@Article{diggle1998,
  author = {P. J. Diggle and R. A. Moyeed and J. A. Tawn},
  title = {Model-Based Geostatistics},
  journal = {Applied Statistics},
  year = {1998},
  volume = {47},
  pages = {299--350},
}

@Book{diggle2007,
  title = {Model-Based Geostatistics},
  publisher = {Springer-Verlag},
  author = {P. J. Diggle and P. J. Ribeiro},
  year = {2007},
}




@Article{gneiting2002,
  title = {Nonseparable, stationary covariance functions for space-time data.},
  author = {Gneiting, T.},
  year = {2002},
  journal = {Journal of the American Statistical Association},
  volume = {97},
  pages = {590-600},
}



@Article{finley2009,
  title = {Improving the Performance of Predictive Process Modeling for Large Datasets},
  author = {Andrew O. Finley and Huiyan Sang and Sudipto Banerjee and Alan E. Gelfand},
  year = {2009},
  journal = {Computational Statistics \& Data Analysis},
  volume = {53},
  number = {8},
  pages = {2873-2884},
}

@Article{finley2012,
  author = {Andrew Finley and Sudipto Banerjee and Alan Gelfand},
  title = {Bayesian Dynamic Modeling for Large Space-Time Datasets Using Gaussian Predictive Processes},
  journal = {Journal of Geographical Systems},
  year = {2012},
  volume = {14},
  number = {1},
  pages = {29-47},
}

@Article{finley2007,
  author	= {Andrew O. Finley and Sudipto Banerjee and Bradley P. Carlin},
  title		= {{\tt spBayes}: An {\tt R} Package for Univariate and Multivariate Hierarchical Point-Referenced Spatial Models},
  journal	= {Journal of Statistical Software},
  year		= {2007},
  volume	= {19},
  number	= {4},
  pages		= {1--24},
  url		= {https://www.jstatsoft.org/v19/i04/}
}

@Article{gelfand1998,
  author = {Alan E. Gelfand and Sujit K. Ghosh},
  title = {Model Choice: A Minimum Posterior Predictive Loss Approach},
  journal = {Biometrika},
  volume = {85},
  pages = {1-11},
  year = {1998},
}

@Article{gelfand2004,
  author = {Alan Gelfand and Alexandra Schmidt and Sudipto Banerjee and C. Sirmans},
  title = {Nonstationary Multivariate Process Modeling through Spatially Varying Coregionalization},
  journal = {Test},
  year = {2004},
  volume = {13},
  number = {2},
  pages = {263-312},
}

@Article{gelfand2005,
  author = {Alan Gelfand and Sudipto Banerjee and D. Gamerman},
  title = {Univariate and Multivariate Dynamic Spatial Modelling},
  journal = {Environmetrics},
  year = {2005},
  volume = {16},
  pages = {465--479},
}

@Book{gelman2004,
  title = {Bayesian Data Analysis},
  author = {A. Gelman and J. B. Carlin and H. S. Stern and D. B. Rubin},
  year = {2004},
  publisher = {Chapman \& Hall/CRC},
}

@Article{gneiting2007,
  author = {Tilmann Gneiting and Adrian E. Raftery},
  journal = {Journal of the American Statistical Association},
  pages = {359--378},
  title = {Strictly Proper Scoring Rules, Prediction, and Estimation},
  volume = {102},
  year = {2007},
}

@Book{gilks1996,
  title = {Markov Chain Monte Carlo in Practice},
  author = {W. R. Gilks and S. Richardson and D. J. Spiegelhalter},
  year = {1996},
  publisher = {Chapman and Hall},
}

@Article{gilksandwild,
  author = {Gilks, W.R. and Wild, P. },
  journal = {Applied Statistics},
  pages = {337-348},
  title = {Adaptive rejection sampling for Gibbs sampling},
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  year = {1992},
}

@book{thebugsbook2013,
  author = {D. Lunn and C. Jackson and N. Best and A. Thomas and D. Spiegelhalter},
  title = {The BUGS Book: A Practical Introduction to Bayesian Analysis},
  year = {2013},
   publisher = {Chapman  \& Hall: Boca Raton},
}


@Article{henderson1981,
  author = {H. V. Henderson and S. R. Searle},
  title = {On Deriving the Inverse of a Sum of Matrices},
  journal = {SIAM Review},
  year = {1981},
  volume = {23},
  pages = {53--60},
  number = {1},
}

@Article{kammann2003,
  author = {E. E. Kammann and M. P. Wand},
  title = {Geoadditive Models},
  year = {2003},
  journal = {Applied Statistics},
  volume = {52},
  pages = {1-18},
}

@Article{lunn2000,
  author = {D. Lunn and D. Spiegelhalter and A. Thomas and N. Best},
  title = {The {\tt BUGS} Project: Evolution, Critique and Future Directions},
  year = {2009},
  journal = {Statistics in Medicine},
  volume = {28},
  pages = {3049-3067},
}

@Article{lin2000,
  author = {Xiwu Lin and Grace Wahba and Dong Xiang and Fangyu Gao and Ronald Klein and Barbara Klein},
  title = {Smoothing Spline ANOVA Models for Large Data Sets with Bernoulli Observations and the Randomized GACV},
  journal = {The Annals of Statistics},
  year = {2000},
  pages = {1570--1600},
}

@Book{moller2003,
  title = {Statistical Inference and Simulation for Spatial Point Processes},
  author = {J. M\o{}ller and R. P. Waagepetersen},
  year = {2003},
  publisher = {Taylor \& Francis},
}

@Book{robert2010,
  title = {Monte Carlo Statistical Methods},
  author = {C. P. Robert and G. Casella},
  year = {2004},
  edition = {2nd},
  publisher = {Springer-Verlag},
}

@Article{roberts2009,
  author = {Gareth O. Roberts and Jeffrey S. Rosenthal},
  journal = {Journal of Computational and Graphical Statistics},
  number = {2},
  pages = {349--367},
  title = {Examples of Adaptive {MCMC}},
  volume = {18},
  year = {2009},
}

@Article{royle1998,
  author = {J. Royle and D. Nychka},
  journal = {Computers \& Geosciences},
  number = {5},
  pages = {479--488},
  title = {An Algorithm for the Construction of Spatial Coverage Designs with Implementation in {\tt S-PLUS}},
  volume = {24},
  year = {1998},
}

@Book{schabenberger2004,
  title = {Statistical Methods for Spatial Data Analysis},
  author = {O. Schabenberger and C. A. Gotway},
  year = {2004},
  publisher = {Taylor \& Francis},
}

@Article{Spiegelhalter2002,
  author = {S. D. Spiegelhalter and N. G. Best and B. P. Carlin and A. V. D. Linde},
  journal = {Journal of the Royal Statistical Society B},
  number = {4},
  pages = {583--639},
  title = {Bayesian Measures of Model Complexity and Fit},
  volume = {64},
  year = {2002},
}

@Article{Stroud2001,
  author = {Jonathan Stroud and Peter M\"{u}ler and Bruno Sans\'o},
  title = {Dynamic Models for Spatio-Temporal Data},
  journal = {Journal of the Royal Statistical Society B},
  year = {2001},
  volume = {63},
  pages = {673--689},
}


@Book{wackernagel2003,
  title = {Multivariate Geostatistics},
  author = {H. Wackernagel},
  year = {2003},
  publisher = {Springer-Verlag},
}

@Book{west1997,
  title = {Bayesian Forecasting and Dynamic Models},
  author = {M. West and J. Harrison},
  year = {1997},
  edition = {2nd},
  publisher = {Springer-Verlag},
}



@article {crimp2014bayesian,
author = {Crimp, Steven and Bakar, Khandoker Shuvo and Kokic, Philip and Jin, Huidong and Nicholls, Neville and Howden, Mark},
title = {Bayesian Space-Time Model to Analyse Frost Risk for Agriculture in Southeast Australia},
journal = {International Journal of Climatology},
publisher = {John Wiley & Sons, Ltd},
issn = {1097-0088},
url = {https://dx.doi.org/10.1002/joc.4109},
doi = {10.1002/joc.4109},
year = {2014},
}



@article {Bakaretal_spTdynamic,
author = {Khandoker Shuvo Bakar and  Philip Kokic and Huidong Jin},
title = {Hierarchical spatially varying coefficient and temporal dynamic process models using spTDyn},
journal = {Journal of Statistical Computation and Simulation},
year = {2016},
volume ={86},
pages ={820-840},
doi ={10.1080/00949655.2015.1038267},
}

@article {Bakaretal_jrssc,
author = {Khandoker Shuvo Bakar and  Philip Kokic and Warren Jin},
title = {A spatio-dynamic model for assessing frost risk in south-eastern Australia},
journal = {Journal of the Royal Statistical Society, Series C},
year = {2015},
volume ={64},
pages ={755-778},
doi ={10.1111/rssc.12103},
}
Bakar, K. S., Kokic, P. and Jin, H. (2015). A spatio-dynamic model for assessing frost risk in south-eastern Australia. Journal of the Royal Statistical Society, Series C. Bakar, K. S., Kokic, P. and Jin, H. (2015).

@Book{anderson,
  author =    {Anderson, E and Bai, Z and Bischof, C and Blackford, S and Demmel, J and Dongarra, J and Du Croz, J and Greenbaum, A and Hammarling, S and McKenney, A and Sorensen, D},
  title = 	 {\proglang{LAPACK} Users Guide},
  publisher = 	 {Society for Industrial and Applied Mathematics},
  year =         {1999},
  address = 	 { Philadelphia, PA},
  edition = 	 {Third},
}

@Book{bakar12,
  author =     {Bakar, Khandoker Shuvo},
  title = 	 {Bayesian Analysis of Daily Maximum Ozone Levels},
  publisher = 	 {PhD Thesis, University of Southampton},
  year =         {2012},
  address = 	 {Southampton, United Kingdom},
}


@Article{ban,
  author =       {Banerjee, Sudipto and Gelfand, Alan E  and Finley, Andrew O and  Sang, H },
  title =        {Gaussian Predictive Process Models for Large Spatial Data Sets},
  journal =     {Journal of the Royal Statistical Society B},
  year =         {2008},
  volume =    {70},
  pages =     {825-848},
}

@Book{ban2,
  author =       {Banerjee, Sudipto and Carlin, Bradley P and Gelfand, Alan E},
  title = 	 {Hierarchical Modeling and Analysis for Spatial Data},
  publisher = 	 {Chapman \& Hall/CRC},
  year =         {2004},
  address = 	 {Boca Raton},
}

@Article{black,
  author =   {Blackford, S L and Demmel, J and Dongarra, J and Duff, I and Hammarling, S and Henry, G and Heroux, M and Kaufman, L and Lumsdaine, A and Petitet, A and Pozo, R and Remington, K and Whaley, C R},
  title =        {An Updated Set of Basic Linear Algebra Subprograms (\proglang{BLAS})},
  journal =     {Transactions on Mathematical Software},
  year =         {2002},
  volume =    {28},
  number =    {2},
  pages =     {135-151},
}


@ARTICLE{coda2,
  AUTHOR = {Martyn Plummer and Nicky Best and Kate Cowles and Karen Vines},
  TITLE = {{\tt CODA}: Convergence Diagnosis and Output Analysis for MCMC},
  JOURNAL = {\proglang{R} News},
  YEAR = 2006,
  VOLUME = 6,
  NUMBER = 1,
  PAGES = {7--11},
  MONTH = {March},
  URL = {https://cran.r-project.org/doc/Rnews},
}

@article{cameletti,
  author =    {Cameletti, M and Ignaccolo, R and Bande, S},
  title =        {Comparing Air Quality Statistical Models},
  journal =     {Technical Report},
  year =         {2009},
  note =      {University of Bergamo, Italy.},
}


@Book{cressie,
  author =       {Cressie, N A C},
  title = 	 {Statistics for Spatial Data},
  publisher = 	 {John Wiley \& Sons},
  year =         {1993},
  address = 	 {New York},
}



@article{cressie2,
  author =    {Cressie, N A C and Johannesson, G},
  title =        {Fixed Rank Kriging for Very Large Spatial Data Sets},
  journal =     {Journal of the Royal Statistical Society B},
  year =         {2008},
  volume =    {70},
  pages =     {209-226},
}


@article{finley,
  author =    {Finley, Andrew O and Banerjee, Sudipto and  Carlin, Bradley P},
  title =        {{\tt spBayes}: An \proglang{R} Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models},
  journal =     {Journal of Statistical Software},
  year =         {2007},
  volume =    {12},
  number =    {4},
  pages =     {1-24},
}

@article{finley2,
  author =       {Finley, Andrew O and Sang, H and Banerjee, Sudipto and Gelfand, A E},
  title =        {Improving the Performance of Predictive Process Modeling for Large Datasets},
  journal =     {Computational Statistics and Data Analysis},
  year =         {2009},
  volume =    {53},
  pages =     {2873-2884},
}


@article{geman,
  author =       {Geman, S and Geman, D},
  title =        {Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images},
  journal =     {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year =         {1984},
  volume =    {6},
  number =    {6},
  pages =     {721-741},
}

@article{gelfand,
  author =       {Gelfand, Alan E and Smith, Adrian F M},
  title =        {Sampling-Based Approaches to Calculating Marginal Densities},
  journal =     {Journal of the American Statistical Association},
  year =         {1990},
  volume =    {85},
  number =    {410},
  pages =     {398-409},
}

@article{gelfand2012,
  author =       {Gelfand, Alan E},
  title =        {Hierarchical Modeling for Spatial Data Problems},
  journal =     {Spatial Statistics},
  year =         {2012},
  volume =    {1},
  pages =     {30-39},
}

@Book{gelfand2010,
  author =     {Gelfand, A E and  Diggle, P J and  Fuentes, M and Guttorp, P},
  title = 	 {The Handbook of Spatial Statistics},
  publisher = 	 {Chapman \& Hall/CRC},
  year =         {2010},
  address = 	 {New York},
}


@article{gelman92,
  author =       {Gelman, A and Rubin, D B},
  title =        {Inference from iterative simulation using multiple sequences},
  journal =     {Statistical Science},
  year =         {1992},
  volume =    {7},
  number =    {4},
  pages =     {457-472},
}

@Book{gelman2004,
  author =       {Gelman, Andrew and Carlin, John B and Stern, Hal S and Rubin, Donald B},
  title = 	 {Bayesian Data Analysis},
  publisher = 	 {Chapman \& Hall/CRC},
  year =         {2004},
  address = 	 {Boca Raton},
  edition = 	 {2nd},
}


@article{hand,
  author =       {Handcock, M S and Stein, M L},
  title =        {A Bayesian Analysis of Kriging},
  journal =     {Technometrics},
  year =         {1993},
  volume =    {35},
  pages =     {403-410},
}

@article{hand2,
  author =       {Handcock, M S and Wallis, J},
  title =        {An Approach to Statistical Spatial-Temporal Modelling of Meteorological Fields},
  journal =     {Journal of the American Statistical Association},
  year =         {1994},
  volume =    {89},
  pages =     {368-390},
}

@Book{hastieGAM90,
  author =       {Hastie, Trevor and Tibshirani, Robert },
  title = 	 {Generalized Additive Models},
  publisher = 	 {Chapman \& Hall},
  year =         {1990},
  address = 	 {London},
}


@article{johannesson,
  author =   {Johannesson, G and Cressie, N},
  title =        {Finding Large-Scale Spatial Trends in Massive, Global, Environmental Datasets},
  journal =     {Environmetrics},
  year =         {2004},
  volume =    {15},
  pages =     {1-44},
}

@article{krige51,
  author =       {Krige, D G},
  title =        {A Statistical Approach to Some Basic Mine Valuation Problems on the Witwatersrand},
  journal =     {Journal of the Chemical, Metallurgical and Mining Society of South Africa},
  year =         {1951},
  volume =    {52},
  pages =     {119-139},
}



@inbook{lasinio,
  author =	 {Lasinio, G Jona and Sahu, S K and Mardia, K V},
  title =	  {Bayesian Statistics and its Applications},
  address =	 {London},
  chapter = {1},
  publisher =	 {Eds. S K Upadhyay, U Singh and D K Dey, Anshan Ltd.},
  year =	 {2007},
}

@Book{matern,
  author =       {Mat\'{e}rn, B},
  title = 	 {Spatial Variation},
  publisher = 	 {Springer-Verlag},
  year =         {1986},
  address = 	 {Berlin},
  edition = 	 {Second},
  url = {https://asdar-book.org/},
}


@article{raftery92,
  author =       {Raftery, A E and Lewis, S M},
  title =        {One long run with diagnostics: Implementation strategies for Markov chain Monte Carlo},
  journal =     {Statistical Science},
  year =         {1992},
  volume =    {7},
  pages =     {493-497},
}

@article{ribeiro,
  author =       {Ribeiro, Paulo J and Diggle, Peter J},
  title =        {{\tt geoR}: Analysis of Geostatistical Data},
  journal =     {\proglang{R} News},
  year =         {2001},
  volume =    {1},
  number =    {2},
  pages =     {14-18},
}


@Book{rue,
  author =   {Rue, H and Held, L},
  title =  {Gaussian Markov Random Fields: Theory and Applications},
  publisher = 	 {Chapman and Hall/CRC},
  year =         {2006},
  address = 	 {Boca Raton},
}


@Book{shekhar,
  author =       {Shekhar, S and Xiong, H},
  title = 	 {Encyclopedia of GIS},
  publisher = 	 {Springer-Verlag},
  year =         {2008},
  address = 	 {New York},
  url = {https://asdar-book.org/},
}


@article{sahu2,
  author =       {Sahu, Sujit K and Gelfand, Alan E and Holland, David M},
  title =        {Fusing Point and Areal Level Space-Time Data with Application to Wet Deposition},
  journal =     {Journal of the Royal Statistical Society C},
  year =         {2010},
  volume =    {59},
  pages =     {77-103},
}



@article{sahu4,
  author =       {Sahu, S K and Mardia, K V},
  title =        { A Bayesian Kriged-Kalman Model for Short-term Forecasting of Air Pollution Levels},
  journal =     {Journal of the Royal Statistical Society C},
  year =         {2005},
  volume =    {54},
  pages =     {223-244},
}

@article{sahu5,
  author =       {Sahu, S K and Yip, S and Holland, D M},
  title =        {Improved Space-Time Forecasting of Next Day Ozone Concentrations in the Eastern U.S.},
  journal =     {Atmospheric Environment},
  year =         {2009},
  volume =    {43},
  pages =     {494-501},
}



@article{stein,
  author =    {Stein, M L},
  title =        {A Modelling Approach for Large Spatial Datasets},
  journal =     {Journal of the Korian Statistical Society},
  year =         {2008},
  volume =    {37},
  pages =     {3-10},
}


@Book{stein1999,
  author =       {Stein, M L},
  title = 	 {Statistical Interpolation of Spatial Data: Some Theory for Kriging},
  publisher = 	 {Springer-Verlag},
  year =         {1999},
  address = 	 {New York},
    url = {https://asdar-book.org/},
}


@article{winbugs1,
  author =    {Lunn, D J and Thomas, A and Best, N and Spiegelhalter, D},
  title =        {WinBUGS - a Bayesian modelling framework: concepts, structure, and extensibility},
  journal =     {Statistics and Computing},
  year =         {2000},
  volume =    {10},
  pages =     {325-337},
}

@Manual{winbugs2,
  title  = {WinBUGS Version 1.2 User Manual},
  author = {Spiegelhalter, D J and Thomas, A and Best, N G},
  year   = {1999},
  note   = {MRC Biostatistics Unit},
  url    = {https://www.mrc-bsu.cam.ac.uk/bugs},
}


%%%%%%%% R packages



@Book{sp2,
    author = {Roger S. Bivand and Edzer Pebesma and Virgilio
      Gomez-Rubio},
    title = {Applied Spatial Data Analysis with {R}, Second edition},
    year = {2013},
    publisher = {Springer-Verlag},
    address = {New York},
    url = {https://asdar-book.org/},
  }

%% spacetime
@Article{spacetime1,
    title = {{\tt spacetime}: Spatio-Temporal Data in \proglang{R}},
    author = {Edzer Pebesma},
    journal = {Journal of Statistical Software},
    year = {2012},
    volume = {51},
    number = {7},
    pages = {1--30},
  }

%% splm
@Article{splm1,
    title = {\pkg{splm}: Spatial Panel Data Models in \proglang{R}},
    author = {Giovanni Millo and Gianfranco Piras},
    journal = {Journal of Statistical Software},
    year = {2012},
    volume = {47},
    number = {1},
    pages = {1--38},
  }


@Manual{bakarsahu12,
  title  = {\pkg{spTimer}: Spatio-Temporal Bayesian Modelling Using \proglang{R}},
  author = {Bakar, Khandoker Shuvo and Sahu, Sujit Kumar},
  year   = {2013},
  note   = {\proglang{R}~package},
  url    = {https://cran.r-project.org/web/packages/spTimer},
}

@Manual{rprog,
title = {\proglang{R}: A Language and Environment for Statistical Computing},
author = {{\proglang{R} Development Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2006},
note = {{ISBN} 3-900051-07-0},
url = {https://www.R-project.org},
}

@Manual{geoR,
  author =   {Ribeiro, Paulo J and Diggle, Peter J},
  title =       {\pkg{geoR}: Analysis of Geostatistical Data},
  year   = {2012},
  note   = {\proglang{R}~package version~1.7-4},
  url    = {https://cran.r-project.org/web/packages/geoR},
}

@Article{geoR1,
    title = {\pkg{geoR}: A Package for Geostatistical Analysis},
    author = {Ribeiro, P J and Diggle, P J},
    journal = {R-NEWS},
    year = {2001},
    month = {June},
    volume = {1},
    number = {2},
    pages = {14--18},
    note = {ISSN 1609-3631},
  }


@Manual{coda1,
  title  = {\pkg{coda}: Output Analysis and Diagnostics for MCMC},
  author = {Martyn Plummer and Nicky Best and Kate Cowles and Karen Vines},
  year   = {2012},
  note   = {\proglang{R}~package version~0.16-1},
  url    = {https://cran.r-project.org/web/packages/coda},
}

@Manual{fields1,
  title  = {{\tt fields}: Tools for Spatial Data},
  author = {Furrer, Reinhard and Nychka, Douglas and Sain, Stephen},
  year   = {2013},
  note   = {\proglang{R}~package version~6.9.1},
  url    = {https://cran.r-project.org/web/packages/fields},
}

@Manual{fields2,
  author =   {Fields-Development-Team},
  title = 	 {{\tt fields}: Tools for Spatial Data},
  publisher = {National Center for Atmospheric Research},
  year =         {2006},
  address = 	 {Boulder, CO.},
  organization ={University of Colorado},
  URL = {https://www.cgd.ucar.edu/Software/Fields}
}

@Manual{sp,
  author =   {Pebesma, E and Bivand, B and Rowlingson, B and Rubio, V Gomez},
  title =       {\pkg{sp}: Classes and Methods for Spatial Data},
  year   = {2012},
  note   = {\proglang{R}~package version~1.0-5},
  url    = {https://cran.r-project.org/web/packages/sp},
}

@Manual{spacetime,
  author =   {Pebesma, E and Graeler, B and Gottfried, T and Hijmans, RJ},
  title =       {\pkg{spacetime}: Classes and Methods for Spatial Data},
  year   = {2012},
  note   = {\proglang{R}~package version~1.0-3},
  url    = {https://cran.r-project.org/web/packages/spacetime},
}

@Book{rogerbook13,
    author = {Roger S. Bivand and Edzer Pebesma and Virgilio
      Gomez-Rubio},
    title = {Applied Spatial Data Analysis with {R}, Second edition},
    year = {2013},
    publisher = {Springer-Verlag},
    address = {New York},
    url = {https://asdar-book.org/},
  }

@Manual{gstat,
  author =   {Pebesma, Edzer},
  title =       {\pkg{gstat}: Spatial and Spatio-temporal Geostatistical Modelling, Prediction and Simulation},
  year   = {2013},
  note   = {\proglang{R}~package version~1.0-15},
  url    = {https://cran.r-project.org/web/packages/gstat},
}

@Manual{splm,
  author =   {Millo, Giovanni and Piras Gianfranco},
  title =       {\pkg{splm}: Econometric Models for Spatial Panel Data},
  year   = {2012},
  note   = {\proglang{R}~package version~1.0-00},
  url    = {https://cran.r-project.org/web/packages/splm},
}

@Manual{spatial,
  author =   {Ripley, Brian},
  title =       {\pkg{spatial}: Functions for Kriging and Point Pattern Analysis},
  year   = {2013},
  note   = {\proglang{R}~package version~7.3-7},
  url    = {https://cran.r-project.org/web/packages/spatial},
}

@Book{spatial1,
    title = {Modern Applied Statistics with \proglang{S}},
    author = {W. N. Venables and B. D. Ripley},
    publisher = {Springer-Verlag},
    edition = {Fourth},
    address = {New York},
    url = {https://asdar-book.org/},
    year = {2002},
  }

@Manual{spatstat,
  author =   {Baddeley, Adrian and Turner, Rolf},
  title =       {\pkg{spatstat}: Spatial Point Pattern Analysis, Model-fitting, Simulation, Tests},
  year   = {2013},
  note   = {\proglang{R}~package version~1.34-1},
  url    = {https://cran.r-project.org/web/packages/spatstat},
}

@Article{spatstat1,
    title = {\pkg{spatstat}: An \proglang{R} Package for Analyzing Spatial Point
      Patterns},
    author = {Adrian Baddeley and Rolf Turner},
    journal = {Journal of Statistical Software},
    year = {2005},
    volume = {12},
    number = {6},
    pages = {1--42},
 }

@Article{spatstat2,
    title = {Hybrids of Gibbs Point Process Models and Their Implementation},
    author = {Adrian Baddeley and Rolf Turner and Jorge Mateu and  Andrew Bevan},
    journal = {Journal of Statistical Software},
    year = {2013},
    volume = {55},
    number = {11},
    pages = {1--43},
  }


@Manual{mcmcpack,
  author =   {Martin, AD and Quinn, KM and Park, JH},
  title =       {\pkg{MCMCpack}: Markov chain Monte Carlo (MCMC) Package},
  year   = {2012},
  note   = {\proglang{R}~package version~1.2-4},
  url    = {https://cran.r-project.org/web/packages/MCMCpack},
}
@Article{mcmcpack1,
    title = {\pkg{MCMCpack}: Markov Chain Monte Carlo in \proglang{R}},
    author = {Andrew D. Martin and Kevin M. Quinn and Jong Hee Park},
    journal = {Journal of Statistical Software},
    year = {2011},
    volume = {42},
    number = {9},
    pages = {22},
  }

@Manual{mcmcglmm,
  author =   {Hadfield, Jarrod},
  title =       {\pkg{MCMCglmm}: MCMC Generalised Linear Mixed Models},
  year   = {2012},
  note   = {\proglang{R}~package version~2.17},
  url    = {https://cran.r-project.org/web/packages/MCMCglmm},
}
@Article{mcmcglmm1,
    title = {MCMC Methods for Multi-Response Generalized Linear Mixed
      Models: The \pkg{MCMCglmm} \proglang{R} Package},
    author = {Jarrod D Hadfield},
    journal = {Journal of Statistical Software},
    year = {2010},
    volume = {33},
    number = {2},
    pages = {1--22},
  }

@Manual{blr,
  author =   {Campos, Gdelos and Rodriguez, PP},
  title =       {\pkg{BLR}: Bayesian Linear Regression},
  year   = {2012},
  note   = {\proglang{R}~package version~1.3},
  url    = {https://cran.r-project.org/web/packages/BLR},
}

@Manual{gam,
  author =   {Hastie, Trevor},
  title =       {\pkg{gam}: Generalized Additive Models},
  year   = {2013},
  note   = {\proglang{R}~package version~1.09},
  url    = {https://cran.r-project.org/web/packages/gam},
}

@Manual{mgcv,
  author =   {Wood, Simon N},
  title =       {\pkg{mgcv}: Mixed GAM Computation Vehicle with GCV / AIC / REML Smoothness Estimation},
  year   = {2013},
  note   = {\proglang{R}~package version~1.7-27},
  url    = {https://cran.r-project.org/web/packages/mgcv},
}

@Manual{r2winbugs,
  author =   {Liggrs,Uwe},
  title =       {\pkg{R2WinBUGS}: Running WinBUGS and OpenBUGS from R / S-PLUS},
  year   = {2011},
  note   = {\proglang{R}~package version~2.1-18},
  url    = {https://cran.r-project.org/web/packages/R2WinBUGS},
}
@Article{r2winbugs1,
    title = {\pkg{R2WinBUGS}: A Package for Running WinBUGS from \proglang{R}},
    author = {Sibylle Sturtz and Uwe Ligges and Andrew Gelman},
    journal = {Journal of Statistical Software},
    year = {2005},
    pages = {1--16},
    number = {3},
    volume = {12},
  }

@Manual{rjags,
  author =   {Plummer, Martyn},
  title =       {\pkg{rjags}: Bayesian Graphical Models using MCMC},
  year   = {2014},
  note   = {\proglang{R}~package version~3-12},
  url    = {https://cran.r-project.org/web/packages/rjags},
}

@Manual{nlme,
  author =   {Pinheiro, Jose and Bates, Douglas and DebRoy, Saikat and Sarkar, Deepayan},
  title =       {\pkg{nlme}: Linear and Nonlinear Mixed Effects Models},
  year   = {2013},
  note   = {\proglang{R}~package version~3.1-113},
  url    = {https://cran.r-project.org/web/packages/nlme},
}

@Manual{akima,
  author =   {Akima, R},
  title =       {\pkg{akima}: Interpolation of Irregularly Space Data},
  year   = {2013},
  note   = {\proglang{R}~package version~0.5-11},
  url    = {https://cran.r-project.org/web/packages/akima},
}






@article{Piranietal2014,
Author = {Pirani, Monica and Gulliver, John and Fuller, Gary W. and Blangiardo,
   Marta},
Title = {{Bayesian spatiotemporal modelling for the assessment of short-term
   exposure to particle pollution in urban areas}},
Journal = {{Journal of Exposure Science and Environmental Epidemiology}},
Year = {{2014}},
Volume = {{24}},
Number = {{3}},
Pages = {{319-327}},
Month = {{MAY-JUN}},
Abstract = {{This paper describes a Bayesian hierarchical approach to predict
   short-term concentrations of particle pollution in an urban environment,
   with application to inhalable particulate matter (PM10) in Greater
   London. We developed and compared several spatiotemporal models that
   differently accounted for factors affecting the spatiotemporal
   properties of particle concentrations. We considered two main source
   contributions to ambient measurements: (i) the long-range transport of
   the secondary fraction of particles, which temporal variability was
   described by a latent variable derived from rural concentrations; and
   (ii) the local primary component of particles (traffic- and
   non-traffic-related) captured by the output of the dispersion model
   ADMS-Urban, which site-specific effect was described by a Bayesian
   kriging. We also assessed the effect of spatiotemporal covariates,
   including type of site, daily temperature to describe the seasonal
   changes in chemical processes affecting local PM10 concentrations that
   are not considered in local-scale dispersion models and day of the week
   to account for time-varying emission rates not available in emissions
   inventories. The evaluation of the predictive ability of the models,
   obtained via a cross-validation approach, revealed that concentration
   estimates in urban areas benefit from combining the city-scale particle
   component and the long-range transport component with covariates that
   account for the residual spatiotemporal variation in the pollution
   process.}},
DOI = {{10.1038/jes.2013.85}},
ISSN = {{1559-0631}},
EISSN = {{1559-064X}},
Unique-ID = {{ISI:000334571000012}},
}
@article{zhang,
  author =    {Zhang, H},
  title =        {Inconsistent Estimation and Asymptotically Equal Interpolations in Model-based Geostatistics},
  journal =     {Journal of the American Statistical Association},
  year =         {2004},
  volume =    {99},
  pages =     {250-261},
}

@article{sghJasa,
  author =       {Sahu, S. K. and Gelfand, A. E. and Holland, D. M.},
  title =        {High-Resolution Space-Time Ozone Modeling for Assessing Trends},
  journal =     {Journal of the American Statistical Association},
  year =         {2007},
  volume =    {102},
  number =    {},
  month =     {},
  pages =     {1221-1234},
  note =      {},
  annote =    {}
}


@ARTICLE{berrocal2009,
  AUTHOR =       "Berrocal, V and Gelfand, A and Holland, D",
  TITLE =        "A {S}patio-{T}emporal {D}ownscaler for {O}utput {F}rom {N}umerical {M}odels",
  JOURNAL =      "Journal of {A}gricultural, {B}iological and {E}nvironmental {S}tatistics",
  YEAR =         "2009",
  volume =       "15",
  pages =        "176-197",
}

@ARTICLE{berrocal2012,
  AUTHOR =       "Berrocal, V and Gelfand, A and Holland, D",
  TITLE =        "Space-Time Data fusion Under Error in Computer Model Output: An Application to Modeling Air Quality",
  JOURNAL =      "Biometrics",
  YEAR =         "2012",
  volume =       "68",
  pages =        "837-848",
}

@ARTICLE{best2000,
  AUTHOR ="Best, N and Ickstadt, K and Wolpert, R",
  TITLE =        "Spatial {P}oisson {R}egression for {H}ealth and {E}xposure {D}ata {M}easured at {D}isparate {R}esolutions",
  JOURNAL =      "Journal of the {A}merican {S}tatistical {A}ssociation",
  YEAR =         "2000",
  volume =       "95",
  pages =        "1076-1088",
}

@ARTICLE{blangiardo2016,
  AUTHOR ="Blangiardo, M and Finazzi, F and Cameletti, M",
  TITLE =        "Two-stage {B}ayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions",
  JOURNAL =      "Spatial and {S}patio-temporal {E}pidemiology",
  YEAR =         "2016",
  volume =       "18",
  pages =        "1-12",
}

@MISC{defra2014,
   TITLE =       "{Updated projections for Nitrogen Dioxide (NO2) compliance}",
   AUTHOR = "{Department for the {E}nvironment {F}ood and {R}ural {A}ffairs}",
   YEAR   =       "{2015}",
}

@ARTICLE{elliott2007,
  AUTHOR =       "Elliott, P and Shaddick, G and Wakefield, J and Hoogh, C and Briggs, D",
  TITLE =        "Long-term associations of outdoor air pollution with mortality in {G}reat {B}ritain",
  JOURNAL =      "Thorax",
  YEAR =         "2007",
  volume =       "62",
  pages =        "1088-1094",
}

@Manual{fields1,
  title  = {{\tt fields}: Tools for Spatial Data},
  author = {Furrer, Reinhard and Nychka, Douglas and Sain, Stephen},
  year   = {2013},
  note   = {{\tt R}~package version~6.9.1},
  organization ={National Center for Atmospheric Research, Boulder, Colorado},
  url    = {https://cran.r-project.org/web/packages/fields},
}

@ARTICLE{gelfand2001,
 AUTHOR =       "Gelfand, A and Zhu, L and Carlin, B",
 TITLE =        "On the change of support problem for spatio-temporal data ",
 JOURNAL =      "Biostatistics",
 YEAR =         "2001",
 volume =       "2",
 pages =        "31-45",
}

@article{GelfandandGhosh1998,
  author =       {Gelfand, A. E. and Ghosh, S. K.},
  title =        {Model choice: A minimum posterior predictive loss approach},
  journal =     {Biometrika},
  year =         {1998},
  volume =    {85},
  number =    {},
  month =     {},
  pages =     {1-11},
  note =      {},
  annote =    {}
}

@ARTICLE{greven2011,
  AUTHOR =       "Greven, S and Dominici, F and Zeger, S",
  TITLE =        "An {A}pproach to the {E}stimation of {C}hronic {A}ir {P}ollution {E}ffects {U}sing {S}patio-{T}emporal {I}nformation",
  JOURNAL =      "Journal of the {A}merican {S}tatistical {A}ssociation",
  YEAR =         "2011",
  volume =       "106",
  pages =        "396-406",
}

@Article{Guhaniyogi2011,
  author = {Rajarshi Guhaniyogi and Andrew O. Finley and Sudipto Banerjee and Alan E. Gelfand},
  journal = {Environmetrics},
  number = {8},
  pages = {997--1007},
  title = {Adaptive Gaussian Predictive Process Models for Large Spatial Datasets},
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@ARTICLE{haining2010,
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}


@ARTICLE{lee2009,
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}


@ARTICLE{lee2015,
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}



@ARTICLE{mackenbach1997,
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  TITLE =        "Long-{T}erm {E}xposure to {A}ir {P}ollution and {I}ncidence of {C}ardiovascular {E}vents in {W}omen",
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}

@ARTICLE{plummer2008,
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  JOURNAL =      "Biostatistics",
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}



@ARTICLE{rushworth2014,
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}

@ARTICLE{rushworth2016,
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  YEAR    =      "2017",
   volume =       "66",
   pages  =     "141-157"
}

@ARTICLE{sacks2014,
  AUTHOR =       "Sacks,  J and Rappold, A and {Davis Jr}, J and Richardson, D and Waller, A and Luben, T",
  TITLE =        "Influence of {U}rbanicity and {C}ounty {C}haracteristics on the {A}ssociation between {O}zone and {A}sthma {E}mergency {D}epartment {V}isits in {N}orth {C}arolina",
  JOURNAL =      "Environmental {H}ealth {P}erspectives",
  YEAR =         "2014",
  volume =       "122",
  pages =        "506-512"
}

@ARTICLE{sahu2010,
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 YEAR =         "2010",
 volume =       "59",
 pages =        "77-103"
}

@article{SahuandMukhopadhyay2015,
  author =       {Sahu, S. K.  and Mukhopadhyay, S.},
  title =        {On generating a flexible class of anisotropic spatial models using Gaussian predictive processes},
  journal =     {Technical Report, University of Southampton},
  year =         {2015},
  volume =    {},
  number =    {},
  month =     {},
  pages =     {},
  note =      {},
  annote =    {}
}


@ARTICLE{vinikoor2014,
  AUTHOR =       "{Vinikoor-Imler}, L and Davis, J and Meyer, R and Messer, L and Luben, T",
  TITLE =        "Associations between prenatal exposure to air pollution, small for gestational age, and term low birthweight in a state-wide birth cohort",
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  volume =       "132",
  pages =        "132-139",
}

@ARTICLE{wakefield2006,
  AUTHOR =       "Wakefield, J and Shaddick, G",
  TITLE =        "Health exposure modelling and the ecological fallacy",
  JOURNAL =      "Biostatistics",
  YEAR =         "2006",
  volume =       "7",
  pages =        "438-455",
  abstract =     "",
}



@MISC{RCP2016,
   TITLE =       "{Every breath we take: {T}he lifelong impact of air pollution}",
   AUTHOR = "{Royal {C}ollege of {P}hysicians}",
   YEAR   =       "{2016}",
   howpublished = {https://www.rcplondon.ac.uk/projects/outputs/every-breath-we-take-lifelong-impact-air-pollution},
}


@article{Rue_inla,
author = {Rue, Håvard and Martino, Sara and Chopin, Nicolas},
title = {Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations},
journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
volume = {71},
number = {2},
pages = {319-392},
keywords = {Approximate Bayesian inference, Gaussian Markov random fields, Generalized additive mixed models, Laplace approximation, Parallel computing, Sparse matrices, Structured additive regression models},
doi = {https://doi.org/10.1111/j.1467-9868.2008.00700.x},
abstract = {Summary.  Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical and geoadditive models. We consider approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models, where the latent field is Gaussian, controlled by a few hyperparameters and with non-Gaussian response variables. The posterior marginals are not available in closed form owing to the non-Gaussian response variables. For such models, Markov chain Monte Carlo methods can be implemented, but they are not without problems, in terms of both convergence and computational time. In some practical applications, the extent of these problems is such that Markov chain Monte Carlo sampling is simply not an appropriate tool for routine analysis. We show that, by using an integrated nested Laplace approximation and its simplified version, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged.},
year = {2009}
}

@Misc{rstan,
    title = {{RStan}: the {R} interface to {Stan}},
    author = {{Stan Development Team}},
    note = {R package version 2.21.2},
    year = {2020},
    url = {https://mc-stan.org/},
  }


