Generalized linear mixed models: development and comparison of different estimation methods

Abstract

The use of generalized linear mixed models is growing in popularity in the modelling of correlated data. To date, methods available are either computationally intensive or asymptotically biased. The following work examines the performance of three methods through the use of simulation studies: maximum likelihood, approximate maximum likelihood and iterative bias correction. The effects of sample size, the true values of parameters and the distribution of the random effects on the standard errors, bias and mean-squared errors of the resulting estimates are investigated. An improvement to the iterative bias correction method has been proposed to increase the method's computational efficiency.