Some connections between Bayesian and non-Bayesian methods for regression model selection

被引：3

作者：

Liang, FM ^{[1
]}

机构：

[1] Natl Univ Singapore, Dept Stat & Appl Probabil, Singapore 117543, Singapore

来源：

STATISTICS & PROBABILITY LETTERS | 2002年 / 57卷 / 01期

关键词：

Bayes factor; FPE alpha; criterion; Kullback-Leibler distance; MAP; variable selection;

D O I：

10.1016/S0167-7152(02)00048-2

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：

In this article, we study the connections between Bayesian methods and non-Bayesian methods for variable selection in multiple linear regression. We show that each of the non-Bayesian criteria, FPEalpha, AIC, C-p and adjusted R-2, has its Bayesian correspondence under an appropriate prior setting. The theoretical results are illustrated by numerical simulations. (C) 2002 Elsevier Science B.V. All rights reserved.

引用

页码：53 / 63

页数：11

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