Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0167-7152(02)00048-2
Title: Some connections between Bayesian and non-Bayesian methods for regression model selection
Authors: Liang, F. 
Keywords: Bayes factor
FPE2 criterion
Kullback-Leibler distance
MAP
Variable selection
Issue Date: 1-Mar-2002
Citation: Liang, F. (2002-03-01). Some connections between Bayesian and non-Bayesian methods for regression model selection. Statistics and Probability Letters 57 (1) : 53-63. ScholarBank@NUS Repository. https://doi.org/10.1016/S0167-7152(02)00048-2
Abstract: 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, FPEα, AIC, Cp and adjusted ℝ2, has its Bayesian correspondence under an appropriate prior setting. The theoretical results are illustrated by numerical simulations. © 2002 Elsevier Science B.V. All rights reserved.
Source Title: Statistics and Probability Letters
URI: http://scholarbank.nus.edu.sg/handle/10635/105378
ISSN: 01677152
DOI: 10.1016/S0167-7152(02)00048-2
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