Please use this identifier to cite or link to this item: https://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 
Major Field of Science: Medical and Health Sciences
Field Category: Health Sciences
Keywords: Linear regression;Metropolis-within-Gibbs;Probit regression
Issue Date: 20-Sep-2013
Source: Journal of Computational and Graphical Statistics, 2013, vol. 22, no. 3, pp. 729-748
Volume: 22
Issue: 3
Start page: 729
End page: 748
Journal: Journal of Computational and Graphical Statistics 
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.
ISSN: 1061-8600
DOI: 10.1080/10618600.2012.694756
Rights: © Taylor & Francis
Type: Article
Affiliation : Cyprus University of Technology 
University of Kent 
University of Warwick 
Appears in Collections:Άρθρα/Articles

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