Abstract: Generalized linear mixed models (GLMMs) are a widely used class of models for analyzing correlated data in medicine and ecology. As with all regression models though, a key part of the inference process is variable selection. I focus on penalized likelihood methods for performing simultaneous selection of fixed and random effects in GLMMs. In particular, there is often an implicit belief that when fitting GLMMs with random intercepts and slope, covariates should be included in the model as either a fixed effect only, or a composite (i.e. both fixed and random effect) effect. In this talk, I propose a penalty called CREPE that builds the aforementioned belief into the variable selection process. Specifically, CREPE accounts for the hierarchical nature of the covariates, so that the final submodel involves only fixed and/or composite effects. Asymptotic properties of the CREPE estimator are discussed including selection consistency and the oracle property. Simulations demonstrate the strong performance of CREPE compared to some other currently available penalized methods for GLMMs. Biography: Francis Hui is a post-doctoral fellow at the Mathematical Sciences Institute at ANU, working under the guise of Alan Welsh and USYD’s very own superstar Samuel Muller. His interests include mixed models, mixture models, variable selection, and ecological statistics, all whilst drinking copious amounts of tea.