Bayesian Variable Selection for Logistic Models Using Auxiliary Mixture Sampling

Regina Tüchler

    Publication: Working/Discussion PaperWU Working Paper

    14 Downloads (Pure)


    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)
    Original languageEnglish
    Place of PublicationWien
    Publication statusPublished - 1 Oct 2006

    Publication series

    SeriesResearch Report Series / Department of Statistics and Mathematics

    WU Working Paper Series

    • Research Report Series / Department of Statistics and Mathematics

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