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Cross-validation prior choice in Bayesian probit regression with many covariates

Lamnisos, Demetris, Griffin, Jim E., Steel, Mark F.J. (2012) Cross-validation prior choice in Bayesian probit regression with many covariates. Statistics and Computing, 22 (2). pp. 359-373. ISSN 0960-3174. (doi:10.1007/s11222-011-9228-1) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:29599)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1007/s11222-011-9228-1

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: Article
DOI/Identification number: 10.1007/s11222-011-9228-1
Uncontrolled keywords: Bayesian variable selection – Cross-validation – Gene expression data – Importance sampling – Log predictive score – Ridge prior
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Jim Griffin
Date Deposited: 30 May 2012 12:42 UTC
Last Modified: 16 Nov 2021 10:07 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/29599 (The current URI for this page, for reference purposes)

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