Lamnisos, Demetris, Griffin, Jim E. and Steel, Mark F. J.. (2012) Cross-validation prior choice in Bayesian probit regression with many covariates. Statistics and Computing, Vol.22 (No.2). pp. 359-373. ISSN 0960-3174

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Abstract

This paper examines prior choice in probit regression through a predictive cross-validation criterion. In particular, we focus on situations where the number of potential covariates is far larger than the number of observations, such as in gene expression data. Cross-validation avoids the tendency of such models to fit perfectly. We choose the scale parameter c in the standard variable selection prior as the minimizer of the log predictive score. Naive evaluation of the log predictive score requires substantial computational effort, and we investigate computationally cheaper methods using importance sampling. We find that K−fold importance densities perform best, in combination with either mixing over different values of c or with integrating over c through an auxiliary distribution.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	Probits, Bayesian statistical decision theory, Regression analysis, Gene expression -- Data processing
Journal or Publication Title:	Statistics and Computing
Publisher:	Springer
ISSN:	0960-3174
Date:	March 2012
Volume:	Vol.22
Number:	No.2
Page Range:	pp. 359-373
Identification Number:	10.1007/s11222-011-9228-1
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
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URI:	http://wrap.warwick.ac.uk/id/eprint/37681

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