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

被引：95

作者：

Hahn, P. Richard ^{[1
]}

Carvalho, Carlos M. ^{[2
]}

机构：

[1] Univ Chicago, Booth Sch Business, Chicago, IL 60611 USA

[2] Univ Texas Austin, McCombs Sch Business, Stat, Austin, TX 78712 USA

来源：

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION | 2015年 / 110卷 / 509期

关键词：

Decision theory; Linear regression; Loss function; Model selection; Parsimony; Shrinkage prior; Sparsity; Variable selection; VARIABLE-SELECTION; REGRESSION; LASSO; STRATEGIES; ESTIMATOR; MIXTURES; PRIORS;

D O I：

10.1080/01621459.2014.993077

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

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.

引用

页码：435 / 448

页数：14

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