Abstract #301602

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JSM 2002 Abstract #301602
Activity Number: 253
Type: Contributed
Date/Time: Tuesday, August 13, 2002 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Stat. Sciences*
Abstract - #301602
Title: On the Sensitivity of Bayes Factors to the Prior Distributions
Author(s): Sandip Sinharay*+ and Hal Stern
Affiliation(s): Educational Testing Service and Iowa State University
Address: MS-16T, Rosedale Road, Princeton, New Jersey, 08536, usa
Keywords: variance component model ; nested models ; sensitivity analysis ; generalized linear mixed model
Abstract:

The Bayes factor is a Bayesian statistician's tool for model selection. Bayes factors can be highly sensitive to the prior distributions used for the parameters of the models under consideration. We discuss an approach for studying the sensitivity of the Bayes factor to the prior distributions for the parameters in the models being compared. The approach is found to be extremely useful for nested models; it has a graphical flavor, making it more attractive than other common approaches to sensitivity analysis for Bayes factors.

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