Bayesian Variable Selection for Logistic Models Using Auxiliary Mixture Sampling

Tüchler, Regina (2006) Bayesian Variable Selection for Logistic Models Using Auxiliary Mixture Sampling. Research Report Series / Department of Statistics and Mathematics, 31. Department of Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna.

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Abstract

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)

Item Type: Paper
Keywords: Covariance Selection / Markov Chain Monte Carlo / Mixed Effects Model / Parsimony
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Repository Administrator
Date Deposited: 30 Mar 2006 12:49
Last Modified: 22 Oct 2019 00:41
URI: https://epub.wu.ac.at/id/eprint/984

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