Adaptive Monte Carlo for Bayesian Variable Selection in Regression Models

Lamnisos, Demetris and 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, 22 (3). pp. 729-748. ISSN 1061-8600. (doi:https://doi.org/10.1080/10618600.2012.694756) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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http://amstat.tandfonline.com/doi/pdf/10.1080/1061...

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 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.

Item Type: Article
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jim Griffin
Date Deposited: 29 May 2014 15:41 UTC
Last Modified: 29 May 2014 15:41 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/41222 (The current URI for this page, for reference purposes)
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