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A reduced bias method of estimating variance components in generalized linear mixed models

Elizabeth A Claassen, University of Nebraska - Lincoln

Abstract

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 that the Firth-adjusted variance component estimates are substantially less biased than maximum likelihood estimates and that inferences using the Firth estimates maintain their Type I error rates more closely than the standard methods.^

Subject Area

Statistics

Recommended Citation

Claassen, Elizabeth A, "A reduced bias method of estimating variance components in generalized linear mixed models" (2014). ETD collection for University of Nebraska - Lincoln. AAI3618588.
http://digitalcommons.unl.edu/dissertations/AAI3618588

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