JSM 2005 Online Program

Abstract #303104

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	16
Type:	Topic Contributed
Date/Time:	Sunday, August 7, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #303104
Title:	Regression Model Search and Uncertainty with Many Predictors
Author(s):	Chris Hans*+ and Mike West
Companies:	Duke University and Duke University
Address:	Box 90251, Durham, NC, 27708-0251, United States
Keywords:	Model Averaging ; Variable Selection ; Parallel Computing ; Stochastic Search ; Gene Expression Data
Abstract:	Problems of model search in regression with large numbers of candidate models raise challenges for both specification and computation. Model/prior assumptions that encourage (or enforce) sparsity are desirable, if not necessary, in order for currently known model search methods (i.e., stochastic or deterministic) to scale to even modest dimensions. However, even under these assumptions of sparsity, the interesting regions of the model space are too large to search using existing MCMC algorithms, and so novel search methods are needed for the rapid identification of promising models. Our work with large-scale regressions provides examples of how coherent Bayesian models can be developed and applied in problems in high dimensions. We describe a distributed computational ``shotgun stochastic search'' approach to regression model search, and address issues of model averaging for prediction over these large spaces.

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