Adaptive Monte Carlo for binary regression with many regressors
Lamnisos, Demetris, Griffin, Jim E. and Steel, Mark F. J. (2009) Adaptive Monte Carlo for binary regression with many regressors. Working Paper. Coventry: University of Warwick. Centre for Research in Statistical Methodology. Working papers, Vol.2009 (No.41).
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Official URL: http://www2.warwick.ac.uk/fac/sci/statistics/crism...
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|
|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|Status:||Not Peer Reviewed|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|
J. H. Albert and S. Chib (1993). Bayesian analysis of binary and polychotomous response
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