This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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39
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Type:
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Contributed
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Date/Time:
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Sunday, August 1, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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Abstract - #306465 |
Title:
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Stochastic Matching Pursuit for Bayesian Variable Selection
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Author(s):
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Ray-Bing Chen*+ and Chi-Hsiang Chu and Te-You Lai and Ying Nian Wu
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Companies:
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National University of Kaohsiung and National University of Kaohsiung and National University of Kaohsiung and University of California, Los Angeles
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Address:
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, , ,
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Keywords:
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Gibbs sampler ;
Metropolis algorithm ;
Stochastic search variable selection
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Abstract:
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This article proposes a stochastic version of the matching pursuit algorithm for Bayesian variable selection in linear regression. The proposed stochastic matching pursuit algorithm is designed for sampling from the posterior distribution of the coefficients for the purpose of variable selection. The proposed algorithm can be considered a modification of the componentwise Gibbs sampler, because the variables that better align with the current residual vector are given higher probabilities of being visited. The proposed algorithm combines the efficiency of the matching pursuit algorithm and the Bayesian formulation with well defined prior distributions on coefficients. Several simulated examples of small n and large p are used to illustrate the algorithm. These examples show that the algorithm is efficient for screening and selecting variables.
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