ACKNOWLEDGMENT
I would like to acknowledge the immense support and
tutelage of Prof. Andrea Riebler.
REFERENCES
Berger, J. (2006). The Case of Objective Bayesian Analysis.
Bayesian Analysis, 385–402.
Bingham, N. J. & Fry, J. M. (2010). Regression: Linear
Models in Statistics. London, UK: Springer Science &
Business Media, Springer-Verlag.
Carroll, R., Lawson, A. B., Faes, C. Kirby, R. S., Aregay, M.,
& Watjou, K. (2015). Comparing INLA and OpenBUGS
for Hierarchical Poisson Modeling in Disease Mapping.
Spatial and Spatio-temporal Epidemiology, 45–54.
Cowles, M. K. & Carlin, B.P. (1996). Markov chain Monte
Carlo Convergence Diagnostics: A Comparative Review.
Journal of the American Statistical Association, 883–904.
Fox, J. (2015). Applied Regression Analysis and Generalized
Linear Models. Los Angeles: Sage Publications.
Gamerman, D. & Lopes, H.F. (2006). Markov chain Monte
Carlo: Stochastic Simulation for Bayesian Inference. New
York: CRC Press.
Givens, G. H., & Hoeting, J. A. (2012). Computaional
Statistics (Vol 703). N.J.: John Wiley and Sons.
Held, L., Schrodle, B. & Rue, H. (2010). Posterior and Crossvalidatory Predictive Checks: A Comparison of McMC
and INLA in Statistical Modedling and Regression. Physica, 91–110.
Lee, D. (2013). CARBayes: An R package for Bayesian
Spatial Modeling with Conditional Autoregressive Priors.
Journal of Statistical Software, 1–24.
Lunn, D., Spiegelhalter, D., Thomas, A. & Best, N. (2009).
The BUGS Project: Evolution, Critique and Future Directions. Statistics in Medicine, 3049–3067.
Nylander, J.A.; Wilgenbusch, J. C.; Warren, D. L. & Swofford, D. L. (2008). AWTY (Are We There Yet?): A System
for Graphical Exploration of McMC Convergence in
Bayesian Phylogenetics. Bioinformatics, 581–583.
Plummer, M. (2003). JAGS: A program for Analysis of
Bayesian Graphical Models Using Gibbs Sampling .
Statistical Computing, 1–10.
Rue, H. & Held, L. (2005). Gaussian Markov Random
Fields: Theory and Applications. Boca Raton, Florida:
CRC Press.
Rue, H., Martino, & S., Chopin, N. (2009). Approximate Bayesian Inference for Latent Gaussian Models by
Using Integrated Nested Laplace Approximations. Royal
Statistics Society: Series B (Statistical Methodology),
319–392.
Wakefield, J. (2013). Bayesian and Frequentist Regression
Methods. New Yrok: Springer Science and Business.
67
I would like to acknowledge the immense support and
tutelage of Prof. Andrea Riebler.
REFERENCES
Berger, J. (2006). The Case of Objective Bayesian Analysis.
Bayesian Analysis, 385–402.
Bingham, N. J. & Fry, J. M. (2010). Regression: Linear
Models in Statistics. London, UK: Springer Science &
Business Media, Springer-Verlag.
Carroll, R., Lawson, A. B., Faes, C. Kirby, R. S., Aregay, M.,
& Watjou, K. (2015). Comparing INLA and OpenBUGS
for Hierarchical Poisson Modeling in Disease Mapping.
Spatial and Spatio-temporal Epidemiology, 45–54.
Cowles, M. K. & Carlin, B.P. (1996). Markov chain Monte
Carlo Convergence Diagnostics: A Comparative Review.
Journal of the American Statistical Association, 883–904.
Fox, J. (2015). Applied Regression Analysis and Generalized
Linear Models. Los Angeles: Sage Publications.
Gamerman, D. & Lopes, H.F. (2006). Markov chain Monte
Carlo: Stochastic Simulation for Bayesian Inference. New
York: CRC Press.
Givens, G. H., & Hoeting, J. A. (2012). Computaional
Statistics (Vol 703). N.J.: John Wiley and Sons.
Held, L., Schrodle, B. & Rue, H. (2010). Posterior and Crossvalidatory Predictive Checks: A Comparison of McMC
and INLA in Statistical Modedling and Regression. Physica, 91–110.
Lee, D. (2013). CARBayes: An R package for Bayesian
Spatial Modeling with Conditional Autoregressive Priors.
Journal of Statistical Software, 1–24.
Lunn, D., Spiegelhalter, D., Thomas, A. & Best, N. (2009).
The BUGS Project: Evolution, Critique and Future Directions. Statistics in Medicine, 3049–3067.
Nylander, J.A.; Wilgenbusch, J. C.; Warren, D. L. & Swofford, D. L. (2008). AWTY (Are We There Yet?): A System
for Graphical Exploration of McMC Convergence in
Bayesian Phylogenetics. Bioinformatics, 581–583.
Plummer, M. (2003). JAGS: A program for Analysis of
Bayesian Graphical Models Using Gibbs Sampling .
Statistical Computing, 1–10.
Rue, H. & Held, L. (2005). Gaussian Markov Random
Fields: Theory and Applications. Boca Raton, Florida:
CRC Press.
Rue, H., Martino, & S., Chopin, N. (2009). Approximate Bayesian Inference for Latent Gaussian Models by
Using Integrated Nested Laplace Approximations. Royal
Statistics Society: Series B (Statistical Methodology),
319–392.
Wakefield, J. (2013). Bayesian and Frequentist Regression
Methods. New Yrok: Springer Science and Business.
67