Variable selection in linear mixed model: A new algorithm incorporating investigator preference and nonmissingness of data
Minhajuddin, A., & Chakraborty, H. (2008). Variable selection in linear mixed model: A new algorithm incorporating investigator preference and nonmissingness of data. In American Statistical Association, Joint Statistical Meeting, Section on Statistics in Epidemiology, Biometrics Section, Denver, CO, August 3-7,2008,.
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.