Analysis of generalized linear mixed models via a stochastic approximation algorithm with Markov chain Monte-Carlo method

Authors:
Hong-Tu Zhu;Sik-Yum Lee
Affiliations:
Department of Epidemiology and Public Health, Yale School of Medicine, New Haven, CT 06520-8034, USA. htzhu@masal.med.yale.edu;Department of Statistics, The Chinese University of Hong Kong, Shatin, N.T. Hong Kong, People's Republic of China. sylee@sparc2.sta.cuhk.edu.hk
Venue:
Statistics and Computing
Year:
2002

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Abstract

In recent years much effort has been devoted to maximum likelihood estimation of generalized linear mixed models. Most of the existing methods use the EM algorithm, with various techniques in handling the intractable E-step. In this paper, a new implementation of a stochastic approximation algorithm with Markov chain Monte Carlo method is investigated. The proposed algorithm is computationally straightforward and its convergence is guaranteed. A simulation and three real data sets, including the challenging salamander data, are used to illustrate the procedure and to compare it with some existing methods. The results indicate that the proposed algorithm is an attractive alternative for problems with a large number of random effects or with high dimensional intractable integrals in the likelihood function.