Likelihood inference in generalized linear mixed models with two components of dispersion using data cloning

In this talk, I will consider the generalized linear mixed models (GLMMs) with two components of dispersion. The frequentist analysis of GLMM is computationally di cult. On the other hand, the advent of the Markov chain Monte Carlo algorithm has made the Bayesian analysis of GLMM computationally convenient. I use recently proposed data cloning approach, which is computationally convenient, to conduct frequentist analysis of GLMMs with two components of dispersion based on maximum likelihood estimation. I discuss the performance of the proposed estimators through simulation, and also illustrate their use with the well known salamander mating data.