Variable selection via Gibbs sampling

Edward I. George, Robert E. McCulloch

Research output: Contribution to journalArticlepeer-review

1713 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)881-889
Number of pages9
JournalJournal of the American Statistical Association
Issue number423
StatePublished - Sep 1993
Externally publishedYes


  • Data augmentation
  • Hierarchical Bayes
  • Latent variables
  • Mixture
  • Multiple regression

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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