Lamnisos, Demetris, Griffin, Jim E. and Steel, Mark F. J. (2009) Adaptive Monte Carlo for binary regression with many regressors. Working Paper. University of Warwick. Centre for Research in Statistical Methodology, Coventry.

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Abstract

This article describes a method for efficient posterior simulation for Bayesian variable selection in probit regression models with many regressors but few observations. A proposal on model space is described which contains a tuneable parameter. An adaptive approach to choosing this tuning parameter is described which allows automatic, e±cient computation in these models. The methods is applied to the analysis of gene expression data.

Item Type:	Working or Discussion Paper (Working Paper)
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	Regression analysis, Monte Carlo method
Series Name:	Working papers
Publisher:	University of Warwick. Centre for Research in Statistical Methodology
Place of Publication:	Coventry
Date:	2009
Volume:	Vol.2009
Number:	No.41
Number of Pages:	13
Status:	Not Peer Reviewed
Access rights to Published version:	Open Access
Funder:	University of Warwick. Centre for Research in Statistical Methodology
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URI:	http://wrap.warwick.ac.uk/id/eprint/35228

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