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Adaptive Monte Carlo for binary regression with many regressors
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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...
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) | ||||
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Subjects: | Q Science > QA Mathematics | ||||
Divisions: | Faculty of Science, Engineering and Medicine > 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 | ||||
Official Date: | 2009 | ||||
Dates: |
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Volume: | Vol.2009 | ||||
Number: | No.41 | ||||
Number of Pages: | 13 | ||||
Status: | Not Peer Reviewed | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Date of first compliant deposit: | 1 August 2016 | ||||
Date of first compliant Open Access: | 1 August 2016 | ||||
Funder: | University of Warwick. Centre for Research in Statistical Methodology |
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