JSM 2004 Online Program

Abstract #301275

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Activity Number:	15
Type:	Topic Contributed
Date/Time:	Sunday, August 8, 2004 : 2:00 PM to 3:50 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #301275
Title:	Model Selection for a K-level Categorical Regression Variable: Are All K Levels Necessary?
Author(s):	Susan A. Alber*+ and Robert E. Weiss
Companies:	University of California, Los Angeles and University of California, Los Angeles
Address:	4416 Fulton Ave. 10, Sherman Oaks, CA, 91423,
Keywords:	model selection ; hierarchical models
Abstract:	We present a model selection approach for regression with a k-level categorical explanatory variable. The model space (M) contains all possible ways to partition the response means for the k groups into subsets with equal values. A hierarchical model is used with the prior for the k means conditional on M. Several different priors for M are considered. The method will be demonstrated using a six-level categorical treatment variable from a study on the effects of lead exposure.

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