This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 161
Type: Topic Contributed
Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract - #309220
Title: Data Augmentation Strategies for the Bayesian Spatial Probit Regression Model
Author(s): Candace Berrett*+ and Catherine Calder
Companies: The Ohio State University and The Ohio State University
Address: 2110 Ridgeview Rd Apt B, Columbus, OH, 43221,
Keywords: Spatial statistics ; Latent variable methods ; Categorical data ; MCMC

Widely recognized data augmentation methods for the Bayesian probit regression model readily allow commonly used covariance structures in the normal linear model to be adapted to the generalized linear model setting, e.g., the Bayesian spatial probit regression model. However, the variance parameter in this model is not identifiable. This problem can be overcome either by fixing the value of the variance parameter or by specifying proper priors and reporting inferences on the properly normalized regression coefficients. Within the data augmentation MCMC class of algorithms, these approaches can be viewed as conditional and marginal augmentation, respectively, where the non-identifiable variance serves as a working parameter. We compare various versions of these algorithms for the Bayesian spatial probit regression model using a simulation study and an analysis of land cover data.

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