Variable selection using the EM and CEM algorithms in mixtures of linear mixed models

Abstract

In order to deal with data of great complexity, the need to develop new methods for variable selection has emerged. Among the new methods, methods based on penalty functions have received great attention. These methods can be used in complex data problems since they shrink a subset of coefficient estimates to zero, thus removing the associated variables from the model, which allows the identification of the subset of the most relevant explanatory variables and, consequently, drastically reduces the computational burden. In this work we analyse the problem of variable selection in mixtures of linear mixed models. In order to do it, we compare the performance of a penalized likelihood approach for variable selection via the Expectation-Maximization and the Classification Expectation-Maximization algorithms through a simulation study and a real-world application.