Experiences with Approximate Bayes Inference for the Poisson-CAR Model

• Activity Number: 656
• Type: Topic Contributed
• Date/Time: Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Chae Young Lim*+ and Sarat C. Dass and Tapabrata Maiti
• Companies: Michigan State University and Michigan State University and Michigan State University
• Keywords: Approximate Bayes ; Default Prior ; Disease Mapping ; Hierarchical Spatial Models ; Hierarchical Bayes ; Empirical Bayes

Abstract

In many areas of epidemiologic, demographic and geographical research, inference based on hierarchical spatial regression models is popular and important; for example, in disease mapping, environmental and health monitoring studies. Several estimation and inferential procedures have been proposed for these models, utilizing a variety of methods such as estimating equations, empirical Bayes, hierarchical Bayes and newly proposed approximate Bayes method. In this article, we compare and contrast between approximate Bayes, hierarchical Bayes and two other inferential methodologies in the context of hierarchical spatial regression models. The differences have been demonstrated via simulation as well as through real examples.