Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/9637
Title: Adaptive Monte Carlo for Bayesian variable selection in regression models
Authors: Lamnisos, Demetris 
Griffin, Jim E. 
Steel, Mark 
Keywords: Linear regression;Metropolis-within-Gibbs;Probit regression
Category: Health Sciences
Field: Medical and Health Sciences
Issue Date: 17-Dec-2013
Publisher: Taylor & Francis Group
Source: Journal of Computational and Graphical Statistics, 2013, Volume 22, Issue 3, Pages 729-748
metadata.dc.doi: 10.1080/10618600.2012.694756
Abstract: This article describesmethods 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 that contains a tuneable parameter. An adaptive approach to choosing this tuning parameter is described that 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 online as supplementary materials.
URI: http://ktisis.cut.ac.cy/handle/10488/9637
ISSN: 10618600
Rights: © 2013 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.
Type: Article
Appears in Collections:Άρθρα/Articles

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