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Bayesian Variable Selection for Logistic Models Using Auxiliary Mixture Sampling

The paper presents an Markov Chain Monte Carlo algorithm for both variable and covariance selection in the context of logistic mixed effects models. This algorithm allows us to sample solely from standard densities, with no additional tuning being needed. We apply a stochastic search variable approach to select explanatory variables as well as to determine the structure of the random effects covariance matrix. For logistic mixed effects models prior determination of explanatory variables and random effects is no longer prerequisite since the definite structure is chosen in a data-driven manner in the course of the modeling procedure. As an illustration two real-data examples from finance and tourism studies are given. (author's abstract) / Series: Research Report Series / Department of Statistics and Mathematics

Identiferoai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:epub-wu-01_94d
Date January 2006
CreatorsTĂĽchler, Regina
PublisherDepartment of Statistics and Mathematics, WU Vienna University of Economics and Business
Source SetsWirtschaftsuniversität Wien
LanguageEnglish
Detected LanguageEnglish
TypeWorking Paper, NonPeerReviewed
Formatapplication/pdf
Relationhttp://epub.wu.ac.at/984/

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