This paper provides a Monte Carlo approach for achieving maximum likelihood
in generalized linear mixed models. Practical approaches for accessing
convergence and precision of parameters are also discussed. Simulation study
shows unbiased fixed effects parameter estimation with covariance components
estimation comparable to previous study. Application for a small-area
estimation of breast cancer mortality using Poisson distribution is
illustrated.