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Adaptive Monte Carlo for Bayesian variable selection in regression models
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Lamnisos, Demetris, Griffin, Jim E. and Steel, Mark F. J. (2013) Adaptive Monte Carlo for Bayesian variable selection in regression models. Journal of Computational and Graphical Statistics, Volume 22 (Number 3). pp. 729-748. doi:10.1080/10618600.2012.694756 ISSN 1061-8600.
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Official URL: http://dx.doi.org/10.1080/10618600.2012.694756
Abstract
This article describes methods for efficient posterior simulation for Bayesian variable selection in Generalized Linear Models with many regressors but few observations. The algorithms use a proposal on model space which contains a tuneable parameter. An adaptive approach to choosing this tuning parameter is described which allows automatic, efficient computation in these models. The method is applied to examples from normal linear and probit regression. Relevant code and datasets are posted as an online supplement.
Item Type: | Journal Article | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Journal or Publication Title: | Journal of Computational and Graphical Statistics | ||||
Publisher: | American Statistical Association | ||||
ISSN: | 1061-8600 | ||||
Official Date: | 2013 | ||||
Dates: |
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Volume: | Volume 22 | ||||
Number: | Number 3 | ||||
Page Range: | pp. 729-748 | ||||
DOI: | 10.1080/10618600.2012.694756 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
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