Variable Selection via Gibbs Sampling
作者:
EdwardI. George,
RobertE. McCulloch,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1993)
卷期:
Volume 88,
issue 423
页码: 881-889
ISSN:0162-1459
年代: 1993
DOI:10.1080/01621459.1993.10476353
出版商: Taylor & Francis Group
关键词: Data augmentation;Hierarchical Bayes;Latent variables;Mixture;Multiple regression
数据来源: Taylor
摘要:
A crucial problem in building a multiple regression model is the selection of predictors to include. The main thrust of this article is to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure entails embedding the regression setup in a hierarchical normal mixture model where latent variables are used to identify subset choices. In this framework the promising subsets of predictors can be identified as those with higher posterior probability. The computational burden is then alleviated by using the Gibbs sampler to indirectly sample from this multinomial posterior distribution on the set of possible subset choices. Those subsets with higher probability—the promising ones—can then be identified by their more frequent appearance in the Gibbs sample.
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