Robust designs for generalized linear mixed models

Generalized linear mixed models (GLMMs) are commonly used for analyzing clustered correlated discrete binary and count data including longitudinal data and repeated measurements. We explore techniques for the design of experiments, where the design issue is formulated as a decision of choosing the values of the predictor(s) for GLMMs. We investigate sequential design methodologies when the fitted model is possibly of an incorrect parametric form. We assess the performance of the proposed design using a small simulation study.Non UBCUnreviewedAuthor affiliation: Carleton UniversityFacult