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Full-Text Articles in Statistics and Probability
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 …
An Outlier Robust Block Bootstrap For Small Area Estimation, Payam Mokhtarian, Ray Chambers
An Outlier Robust Block Bootstrap For Small Area Estimation, Payam Mokhtarian, Ray Chambers
Payam Mokhtarian
Small area inference based on mixed models, i.e. models that contain both fixed and random effects, are the industry standard for this field, allowing between area heterogeneity to be represented by random area effects. Use of the linear mixed model is ubiquitous in this context, with maximum likelihood, or its close relative, REML, the standard method for estimating the parameters of this model. These parameter estimates, and in particular the resulting predicted values of the random area effects, are then used to construct empirical best linear unbiased predictors (EBLUPs) of the unknown small area means. It is now well known …