The Annals of Statistics

Fully Bayes factors with a generalized g-prior

Yuzo Maruyama and Edward I. George

Full-text: Open access

Abstract

For the normal linear model variable selection problem, we propose selection criteria based on a fully Bayes formulation with a generalization of Zellner’s g-prior which allows for p > n. A special case of the prior formulation is seen to yield tractable closed forms for marginal densities and Bayes factors which reveal new model evaluation characteristics of potential interest.

Article information

Source
Ann. Statist., Volume 39, Number 5 (2011), 2740-2765.

Dates
First available in Project Euclid: 22 December 2011

Permanent link to this document
https://projecteuclid.org/euclid.aos/1324563354

Digital Object Identifier
doi:10.1214/11-AOS917

Mathematical Reviews number (MathSciNet)
MR2906885

Zentralblatt MATH identifier
1231.62036

Subjects
Primary: 62F07: Ranking and selection 62F15: Bayesian inference
Secondary: 62C10: Bayesian problems; characterization of Bayes procedures

Keywords
Bayes factor model selection consistency ridge regression singular value decomposition variable selection

Citation

Maruyama, Yuzo; George, Edward I. Fully Bayes factors with a generalized g -prior. Ann. Statist. 39 (2011), no. 5, 2740--2765. doi:10.1214/11-AOS917. https://projecteuclid.org/euclid.aos/1324563354


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