A simulation comparison on spatial Poisson mixed models

References

• Agarwal, D. K., Gelfand, A. G., Citron-Pousty, S. (2002). Zero-inated models with application to spatial count data. Environmental and Ecological Statistics 9:341–355.
   Google Scholar
• Besag, J. (1975). Statistical analysis of non-lattice data. The Statistician 24:179–195.
   Google Scholar
• Christensen, O. F. (2004). Monte Carlo maximum likelihood in model-based geostatistics. Journal of Computational and Graphical Statistics 13:702–718.
   Google Scholar
• Christensen, O. F., Möller, J., Waagepetersen, R. (2000). Analysis of Spatial Data Using Generalized Linear Mixed Models and Langevin-Type Markov Chain Monte Carlo. (R-00-2009), Denmark: Aalborg University.
   Google Scholar
• Christensen, O. F., Roberts, G. O., Sköld, M. (2006). Robust Markov chain Monte Carlo methods for spatial generalized linear mixed models. Journal of Computational and Graphical Statistics 15:1–17.
   Google Scholar
• Christensen, O. F., Waagepetersen, R. (2002). Bayesian prediction of spatial count data using generalized linear mixed models. Biometrics 58:280–286.
   Google Scholar
• Cressie, N. (1993). Statistics for Spatial Data. rev. ed. New York: Wiley.
   Google Scholar
• Davis, R. A., Dunsmuir, W. T. M., Wang, Y. (2000). On autocorrelation in a Poisson regression model. Biometrika 87:491–505.
   Google Scholar
• De Oliveira, V. (2014). Poisson kriging: A closer investigation. Spatial Statistics 7:1–20.
   Google Scholar
• Diggle, P. J., Ribeiro, P. J. (2007). Model Based Geostatistics. New York: Springer.
   Google Scholar
• Diggle, P. J., Tawn, J. A., Moyeed, R. A. (1998). Model-based geostatistics (with discussion). Journal of the Royal Statistical Society: Series C 47:299–326.
   Google Scholar
• Eidsvik, J., Martino, S., Rue, H. (2009). Approximate Bayesian inference in spatial generalized linear mixed models. Scandinavian Journal of Statistics 36:1–22.
   Google Scholar
• Goovaerts, P. (2005). Geostatistical analysis of disease data: Estimation of cancer mortality risk from empirical frequencies using Poisson kriging. International Journal of Health Geographics 4:31.
   Google Scholar
• Goovaerts, P. (2006). Geostatistical analysis of disease data: Accounting for spatial support and population density in the isopleth mapping of cancer mortality risk using area-to-point Poisson kriging. International Journal of Health Geographics 5:52.
   Google Scholar
• Gotway, C. A., Stroup, W. W. (1997). A generalized linear model approach to spatial data analysis and prediction. Journal of Agricultural, Biological, and Environmental Statistics 2:157–178.
   Google Scholar
• Guillot, G., Lorén, N., Rudemo, M. (2009). Spatial prediction of weed intensities from exact count data and image-based estimates. Journal of the Royal Statistical Society: Series C 58:525–542.
   Google Scholar
• Guyon, X. (1995). Random Fields on a Network: Modeling, Statistics, and Applications. New York: Springer.
   Google Scholar
• Kaiser, M. S., Cressie, N. (1997). Modeling Poisson variables with positive spatial dependence. Statistics and Probability Letters 35:423–432.
   Google Scholar
• McCulloch, C. E. (1997). Maximum likelihood algorithms for generalized linear mixed models. Journal of the American Statistical Association 92:162–170.
   Google Scholar
• McCullagh, P., Nelder, J. A. (1989). Generalized Linear Models. 2nd ed. London: Chapman and Hall.
   Google Scholar
• McShane, L. M., Albert, P. S., Palmatier, M. A. (1997). A latent process regression model for spatially correlated count data. Biometrics 53:698–706.
   Google Scholar
• Mohammadzadeh, M., Hosseini, F. (2011). Maximum likelihood estimation for spatial GLM models. Procedia Environmental Sciences 3:63–68.
   Google Scholar
• Monestiez, P., Dubroca, L., Bonnin, E., Durbec, J. P., Guinet, C. (2006). Geostatistical modelling of spatial distribution of Balaenoptera physalus in the northwestern mediterranean sea from sparse count data and heterogeneous. Ecological Modelling 193:615–628.
   Google Scholar
• Royle, J. A., Wikle, C. K. (2005). Efficient statistical mapping of avian count data. Environmental and Ecological Statistics 12:225–243.
   Google Scholar
• Stein, M. L. (1999). Interpolation for Spatial Data. New York: Springer.
   Google Scholar
• Zhang, H. (2002). On estimation and prediction for spatial generalized linear mixed models. Biometrics 58:129–136.
   Google Scholar