Bootstrap Inference in Generalized Linear Mixed Models
Joint with Eva Cantoni

This talk aims to contribute to the discussion on the inference of Generalized Linear Mixed Models (GLMM).
On a first stage, we review bootstrap schemes in GLMM and, inspired in part by schemes based on the random weighting of the Estimating Equations of a Gaussian Linear Mixed Model (LMM), we formulate a strategy based on (i) random weighting of the contributions to the Joint Likelihood of outcomes and random effects and (ii) the Laplace method for the approximation of integrals.
On a second stage, we focus on the measure of uncertainty for predictions for random effects , customarily carried out via approximations to both Frequentist measures such as the Mean Squared Error of Prediction (MSEP) and others inherited from the Bayesian paradigm, such as the Conditional Variances (CV), and propose an implementation of our bootstrap method as an alternative way of computing these measures.