On Mode Jumping in MCMC for Bayesian Variable Selection within GLMM

Generalized linear mixed models (GLMM) are addressed for inference and prediction in a wide range of different applications providing a powerful scientific tool for the researchers and analysts coming from different fields. At the
same time more sources of data are becoming available introducing a variety of
hypothetical explanatory variables for these models to be considered. Estimation
of posterior model probabilities and selection of an optimal model is thus becoming crucial. We suggest a novel mode jumping MCMC procedure for Bayesian
model averaging and model selection in GLMM.