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.