Areal prediction of survey data using Bayesian spatial generalised linear models

References

• ABS, Statistics 2011. Australian Statistical Geography Standard (ASGS): volume 1main structure and greater Capital city statistical areas. Australian Bureau of Statistics, Canberra, Australia.
   Google Scholar
• Bakar, K. S. 2017. Bayesian Gaussian models for interpolating large-dimensional data at misaligned areal units. In MODSIM2017, 22nd International Congress on Modelling and Simulation, ed. G. Syme, D. Hatton MacDonald, B. Fulton, and J. Piantadosi, 85–91. Canberra: Modelling and Simulation Society of Australia and New Zealand.
   Google Scholar
• Bakar, K. S., and H. Jin. 2018. Spatio-temporal quantitative links between climatic extremes and population flows: a case study in the Murray-Darling basin, Australia. Climatic Change 148 (1–2):139–53.
   Web of Science ®Google Scholar
• Bakar, K. S., and P. Kokic. 2017. Bayesian Gaussian models for point referenced spatial and spatio-temporal data. Journal of Statistical Research 51 (1):17–40.
   Google Scholar
• Bakar, K. S., P. Kokic, and H. Jin. 2015. A spatiodynamic model for assessing frost risk in South-Eastern Australia. Journal of the Royal Statistical Society: Series C (Applied Statistics) 64 (5):755–78.
   Web of Science ®Google Scholar
• Bakar, K. S., P. Kokic, and H. Jin. 2016. Hierarchical spatially varying coefficient and temporal dynamic process models using spTDyn. Journal of Statistical Computation and Simulation 86 (4):820–40.
   Web of Science ®Google Scholar
• Bakar, K. S., and S. K. Sahu. 2015. spTimer: Spatio-temporal Bayesian modelling using R. Journal of Statistical Software 63 (15):1–32.
   Web of Science ®Google Scholar
• Banerjee, S., B. P. Carlin, and A. E. Gelfand. 2014. Hierarchical modeling and analysis for spatial data. Boca Raton, FL: CRC Press.
   Google Scholar
• Banerjee, S., A. O. Finley, P. Waldmann, and T. Ericsson. 2010. Hierarchical spatial process models for multiple traits in large genetic trials. Journal of the American Statistical Association 105 (490):506–21.
   PubMed Web of Science ®Google Scholar
• Bradley, J. R., S. H. Holan, and C. K. Wikle. 2015. Multivariate spatio-temporal models for high-dimensional areal data with application to longitudinal employer-household dynamics. The Annals of Applied Statistics 9 (4):1761–91.
   Web of Science ®Google Scholar
• Bradley, J. R., C. K. Wikle, and S. H. Holan. 2016. Bayesian spatial change of support for count-valued survey data with application to the American community survey. Journal of the American Statistical Association 111 (514):472–87.
   Web of Science ®Google Scholar
• Casella, G., F. J. Girón, M. L. Martínez, and E. Moreno. 2009. Consistency of bayesian procedures for variable selection. The Annals of Statistics 37 (3):1207–28.
   Web of Science ®Google Scholar
• Cressie, N., T. Shi, and E. L. Kang. 2010. Fixed rank filtering for spatio-temporal data. Journal of Computational and Graphical Statistics 19 (3):724–45.
   Web of Science ®Google Scholar
• Cressie, N. A. C. 1993. Statistics for spatial data. New York, NY: John Wiley & Sons.
   Google Scholar
• Cressie, N. A. C., and G. Johannesson. 2008. Fixed rank kriging for very large spatial data sets. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 70 (1):209–26.
   Web of Science ®Google Scholar
• Cressie, N. A. C., and C. K. Wikle. 2011. Statistics for Spatio-Temporal data. New York, NY: John Wiley & Sons.
   Google Scholar
• Crimp, S., K. S. Bakar, P. Kokic, H. Jin, N. Nicholls, and M. Howden. 2015. Bayesian space–time model to analyse frost risk for agriculture in southeast Australia. International Journal of Climatology 35 (8):2092–108.
   Web of Science ®Google Scholar
• Dellaportas, P., J. J. Forster, and I. Ntzoufras. 2002. On Bayesian model and variable selection using mcmc. Statistics and Computing 12 (1):27–36.
   Web of Science ®Google Scholar
• Diggle, P., and S. Lophaven. 2006. Bayesian geostatistical design. Scandinavian Journal of Statistics 33 (1):53–64.
   Web of Science ®Google Scholar
• Diggle, P., and P. J. Ribeiro. 2007. Model-based geostatistics. New York, NY: Springer.
   Google Scholar
• Earnest, A., G. Morgan, K. Mengersen, L. Ryan, R. Summerhayes, and J. Beard. 2007. Evaluating the effect of neighbourhood weight matrices on smoothing properties of conditional autoregressive (CAR) models. International Journal of Health Geographics 6 (1):1.
   PubMed Web of Science ®Google Scholar
• Gelfand, A. E., and S. K. Sahu. 1999. Identifiability, improper priors, and Gibbs sampling for generalized linear models. Journal of the American Statistical Association 94 (445):247–53.
   Web of Science ®Google Scholar
• Gelfand, A. E., L. Zhu, and B. P. Carlin. 2001. On the change of support problem for spatio-temporal data. Biostatistics (Oxford, England) 2 (1):31–45.
   PubMedGoogle Scholar
• Gelman, A. 2006. Prior distributions for variance parameters in hierarchical models (comment on article by browne and draper). Bayesian Analysis 1 (3):515–34.
   Web of Science ®Google Scholar
• Gelman, A., J. B. Carlin, H. S. Stern, and D. B. Rubin. 2014. Bayesian data analysis. Volume 3. Boca Raton, FL: Chapman & Hall/CRC.
   Google Scholar
• Gelman, A., A. Jakulin, M. G. Pittau, and Y.-S. Su. 2008. A weakly informative default prior distribution for logistic and other regression models. The Annals of Applied Statistics 2 (4):1360–83.
   Web of Science ®Google Scholar
• George, E. I., and R. E. McCulloch. 1993. Variable selection via Gibbs sampling. Journal of the American Statistical Association 88 (423):881–9.
   Web of Science ®Google Scholar
• Guhaniyogi, R., A. O. Finley, S. Banerjee, and A. E. Gelfand. 2011. Adaptive Gaussian predictive process models for large spatial datasets. Environmetrics 22 (8):997–1007.
   PubMed Web of Science ®Google Scholar
• Higdon, D. 1998. A process-convolution approach to modelling temperatures in the North Atlantic ocean. Environmental and Ecological Statistics 5 (2):173–90.
   Web of Science ®Google Scholar
• Hughes, J., and M. Haran. 2013. Dimension reduction and alleviation of confounding for spatial generalized linear mixed models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 75 (1):139–59.
   Web of Science ®Google Scholar
• Kang, E. L., and N. Cressie. 2011. Bayesian inference for the spatial random effects model. Journal of the American Statistical Association 106 (495):972–83.
   Web of Science ®Google Scholar
• Katzfuss, M. 2013. Bayesian nonstationary spatial modeling for very large datasets. Environmetrics 24 (3):189–200.
   Web of Science ®Google Scholar
• Katzfuss, M., and N. Cressie. 2012. Bayesian hierarchical spatio-temporal smoothing for very large datasets. Environmetrics 23 (1):94–107.
   Web of Science ®Google Scholar
• Katzfuss, M., and D. Hammerling. 2017. Parallel inference for massive distributed spatial data using low-rank models. Statistics and Computing 27 (2):363–75.
   Web of Science ®Google Scholar
• Kokic, P., S. Crimp, and M. Howden. 2011. Forecasting climate variables using a mixed-effect state-space model. Environmetrics 22 (3):409–19.
   Web of Science ®Google Scholar
• Lee, D., and R. Mitchell. 2012. Boundary detection in disease mapping studies. Biostatistics 13 (3):415–26.
   PubMed Web of Science ®Google Scholar
• Lee, D., A. Rushworth, and S. K. Sahu. 2014. A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution. Biometrics 70 (2):419–29.
   PubMed Web of Science ®Google Scholar
• Nychka, D., S. Bandyopadhyay, D. Hammerling, F. Lindgren, and S. Sain. 2015. A multiresolution Gaussian process model for the analysis of large spatial datasets. Journal of Computational and Graphical Statistics 24 (2):579–99.
   Web of Science ®Google Scholar
• Nychka, D., C. Wikle, and J. A. Royle. 2002. Multiresolution models for nonstationary spatial covariance functions. Statistical Modelling: An International Journal 2 (4):315–31.
   Web of Science ®Google Scholar
• Park, T., and G. Casella. 2008. The Bayesian lasso. Journal of the American Statistical Association 103 (482):681–6.
   Web of Science ®Google Scholar
• Reich, B. J., J. S. Hodges, and V. Zadnik. 2006. Effects of residual smoothing on the posterior of the fixed effects in disease-mapping models. Biometrics 62 (4):1197–206.
   PubMed Web of Science ®Google Scholar
• Rue, H., and L. Held. 2005. Gaussian Markov random fields: theory and applications. Boca Raton, FL: CRC Press.
   Google Scholar
• Sahu, S. K., and K. S. Bakar. 2012. Hierarchical bayesian autoregressive models for large space time data with applications to ozone concentration modelling. Applied Stochastic Models in Business and Industry 28 (5):395–415.
   Web of Science ®Google Scholar
• Sahu, S. K., K. S. Bakar, and N. Awang. 2015. Bayesian forecasting using spatiotemporal models with applications to ozone concentration levels in the Eastern United States. Geometry Driven Statistics 121:260.
   Google Scholar
• Spiegelhalter, D. J., N. G. Best, B. P. Carlin, and A. Linde. 2014. The deviance information criterion: 12 years on. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76 (3):485–93.
   Web of Science ®Google Scholar
• Spiegelhalter, D. J., N. G. Best, B. P. Carlin, and A. Van Der Linde. 2002. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64 (4):583–639.
   Web of Science ®Google Scholar
• Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological) 58 (1):267–88.
   Web of Science ®Google Scholar
• Whitten, G. D., and H. D. Palmer. 1999. Cross-national analyses of economic voting. Electoral Studies 18 (1):49–67.
   Web of Science ®Google Scholar
• Wong, D. 2009. The modifiable areal unit problem (MAUP). In The SAGE handbook of spatial analysis, ed. A. Fotheringham and P. Rogerson, 105–123. London: Sage Publications.
   Google Scholar