Spring Seminar Series - January 31, 2005
University of Minnesota
School of Statistics
College of Liberal Arts
Bayesian Model Selection for Geostatistical Regression Data
Devin S. Johnson
Department of Mathematical Sciences
University of Alaska, Fairbanks
Monday, January 31, 2005
3:30 PM, 115
Ford Hall
Minneapolis, East Bank Campus
Social at 3:00 PM, 300 Ford Hall
Abstract
The
problem of covariate selection for spatial regression models is
considered. Previous research has shown that failure to take spatial
correlation into account can influence the outcome of standard model
selection methods. Often, these standard criteria suggest models that
are too complex in an effort to compensate for spatial correlation
ignored in the selection process. Here calculation of posterior model
probabilities for regression models through a Markov Chain Monte Carlo
(MCMC) method is investigated. In addition, the proposed MCMC algorithm
is modified for covariate selection in spatial generalized linear mixed
models (GLMM). The GLMM analysis makes use of Langevin-Hastings updates
for random effects. These methods are demonstrated with two data sets,
one normally distributed and the other a Poisson spatial GLMM.