Decoupling shrinkage and selection in bayesian linear models: A posterior summary perspective

P. Richard Hahn, Carlos M. Carvalho

Research output: Contribution to journalReview articlepeer-review

43 Scopus citations

Abstract

Selecting a subset of variables for linear models remains an active area of research. This article reviews many of the recent contributions to the Bayesian model selection and shrinkage prior literature. A posterior variable selection summary is proposed, which distills a full posterior distribution over regression coefficients into a sequence of sparse linear predictors.

Original languageEnglish (US)
Pages (from-to)435-448
Number of pages14
JournalJournal of the American Statistical Association
Volume110
Issue number509
DOIs
StatePublished - Jan 2 2015
Externally publishedYes

Keywords

  • Decision theory
  • Linear regression
  • Loss function
  • Model selection
  • Parsimony
  • Shrinkage prior
  • Sparsity
  • Variable selection

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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