Decoupling Shrinkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective

• 成果类型: Review
• 署名作者: Hahn, P. Richard; Carvalho, Carlos M.
• 署名单位: University of Chicago; University of Texas System; University of Texas Austin
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2014.993077
• 发表日期: 2015
• 页码: 435-448
• 关键词: variable-selection; regression; Lasso; strategies; estimator; mixtures; priors

摘要

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.