Fast Bayesian inference in a class of sparse linear mixed effects models

Maria-Zafeiria Spyropoulou, James G. Hopker, Jim E. Griffin

Abstract: Linear mixed effects models are widely used in statistical modelling. We consider a mixed effects model with Bayesian variable selection in the random effects using spike-and-slab priors and develop a optimisation-based inference schemes that can be applied to large data sets. An EM algorithm is proposed for the model with normal errors where the posterior distribution of the variable inclusion parameters is approximated using an Occam’s window approach. Placing this approach within a variational Bayes scheme allows the algorithm to be extended to the model with skew-t errors. The performance of the algorithm is evaluated in a simulation study and applied to a longitudinal model for elite athlete performance in 100 metres track sprinting and weightlifting.