Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation.
Department of Statistics: Technical Reports, No.108
Generalized linear mixed models are a widely used tool for modeling longitudinal data. However, their use is typically restricted to few covariates, because the presence of many predictors yields unstable estimates. The presented approach to the fitting of generalized linear mixed
models includes an L1-penalty term that enforces variable selection and shrinkage simultaneously. A gradient ascent algorithm is proposed that allows to maximize the penalized loglikelihood yielding models with reduced complexity. In contrast to common procedures it can be used in high-dimensional settings where a large number of otentially influential explanatory variables is available. The method is investigated in simulation studies and illustrated by use of real data sets.
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Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation. (deposited 06. Jun 2011 13:55)