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Bayesian Variable Selection for Logistic Models Using Auxiliary Mixture Sampling

Content Provider	Scilit
Author	Tüchler, Regina
Copyright Year	2008
Description	The paper presents an Markov Chain Monte Carlo algorithm for both variable and covariance selection in the context of logistic mixed eects,models. This algorithm allows us to sample solely from stan- dard 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 eects,covariance matrix. For logistic mixed eects,models prior determination of explanatory variables and random eects,is no longer prerequisite since the definite structure is chosen in a data-driven manner in the course of the model- ing procedure. As an illustration two real-data examples from finance and tourism studies are given. Regina T¨uchler is an Assistant Professor, Department of Statistics and Mathe- matics, Vienna University of Economics and Business Administration, Austria (E-mail: regina.tuechler@wu-wien.ac.at) 1 KEY WORDS: Covariance Selection, Markov Chain Monte Carlo, Mixed Eects Model, Parsimony
Related Links	http://epub.wu.ac.at/984/1/document.pdf
Ending Page	94
Page Count	19
Starting Page	76
ISSN	10618600
e-ISSN	15372715
DOI	10.1198/106186008x289849
Journal	Journal of Computational and Graphical Statistics
Issue Number	1
Volume Number	17
Language	English
Publisher	Informa UK Limited
Publisher Date	2008-03-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Computational and Graphical Statistics Mathematical Social Sciences Parsimony Mixed Effects Model Markov Chain Monte Carlo
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Discrete Mathematics and Combinatorics Statistics, Probability and Uncertainty

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