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

Lamnisos, Demetris and Griffin, Jim E. and 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. (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)

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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
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: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jim Griffin
Date Deposited: 30 May 2012 12:42
Last Modified: 21 May 2014 11:24
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