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.

KEYWORDS:

• EM algorithm
• CEM algorithm
• mixture models
• penalized likelihood
• variable selection

Acknowledgments

The collection of data used in this study was partly supported by the National Institutes of Health under grant number R01 HD069609 and R01 AG040213, and the National Science Foundation under award numbers SES 1157698 and 1623684.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The research of L. Novais was financed by FCT - Fundação para a Ciência e a Tecnologia, through the PhD scholarship with reference SFRH/BD/139121/2018. The collection of data used in this study was partly supported by the National Institutes of Health [grant number R01 HD069609 and R01 AG040213], and the National Science Foundation [award numbers SES 1157698 and 1623684].