Bayesian Inference for Geostatistical Regression Models
نویسنده
چکیده
The problem of simultaneous covariate selection and parameter inference 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 parameter estimates and 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.
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