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A Reduced Bias Method Of Estimating Variance Components In Generalized Linear Mixed Models, Elizabeth A. Claassen
A Reduced Bias Method Of Estimating Variance Components In Generalized Linear Mixed Models, Elizabeth A. Claassen
Department of Statistics: Dissertations, Theses, and Student Work
In small samples it is well known that the standard methods for estimating variance components in a generalized linear mixed model (GLMM), pseudo-likelihood and maximum likelihood, yield estimates that are biased downward. An important consequence of this is that inferences on fixed effects will have inflated Type I error rates because their precision is overstated. We introduce a new method for estimating parameters in GLMMs that applies a Firth bias adjustment to the maximum likelihood-based GLMM estimating algorithm. We apply this technique to one- and two-treatment logistic regression models with a single random effect. We show simulation results that demonstrate …