Nelson, Kerrie P2009-10-062009-10-062002b4907543351889193Thesis 51878http://hdl.handle.net/1773/8960Thesis (Ph. D.)--University of Washington, 2002The 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.xiv, 184 p.en-USCopyright is held by the individual authors.For information on access and permissions, please see http://digital.lib.washington.edu/rw-faq/rights.htmlTheses--StatisticsThesis