Variable selection in linear mixed model

Abstract

Variable selection in the context of a linear model or a linear mixed model is a fundamental but often a contentious part in the applied statistical model building. However, very little on the topic is available in statistical literature. In the current article, we propose a new algorithm for variable selection in the context of a linear mixed model that considers investigator preference and data availability along with other statistical consideration. Th e performance of the new algorithm is contrasted with the available automated variable selection approaches like stepwise, forward selection, and backward elimination and the best subset selection using a real data set. Cross-validation method is used to assess the predictive performance of the estimated model.