Abstract
Abstract 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.
Keywords
Gibbs samplingSelection (genetic algorithm)Multinomial logistic regressionMultinomial distributionStatisticsMathematicsLatent variableProbabilistic logicPosterior probabilityComputer scienceCovariateEconometricsMachine learningBayesian probability
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Publication Info
- Year
- 1993
- Type
- article
- Volume
- 88
- Issue
- 423
- Pages
- 881-889
- Citations
- 2675
- Access
- Closed
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Cite This
Edward I. George,
Robert E. McCulloch
(1993).
Variable Selection via Gibbs Sampling.
Journal of the American Statistical Association
, 88
(423)
, 881-889.
https://doi.org/10.1080/01621459.1993.10476353
Identifiers
- DOI
- 10.1080/01621459.1993.10476353