Resource title

Bayesian Variable Selection for Logistic Models Using Auxiliary Mixture Sampling

Resource image

image for OpenScout resource :: Bayesian Variable Selection for Logistic Models Using Auxiliary Mixture Sampling

Resource description

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

Resource author

Regina Tüchler

Resource publisher

Resource publish date

Resource language

en

Resource content type

application/pdf

Resource resource URL

http://epub.wu.ac.at/984/1/document.pdf

Resource license

Adapt according to the license agreement. Always reference the original source and author.