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arXiv · 2308.01518

The use of the EM algorithm for regularization problems in high-dimensional linear mixed-effects models

Daniela C. R. Oliveira·Fernanda L. Schumacher·Victor H. Lachos
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Abstract

The EM algorithm is a popular tool for maximum likelihood estimation but has not been used much for high-dimensional regularization problems in linear mixed-effects models. In this paper, we introduce the EMLMLasso algorithm, which combines the EM algorithm and the popular and efficient R package glmnet for Lasso variable selection of fixed effects in linear mixed-effects models. We compare the performance of our proposed EMLMLasso algorithm with the one implemented in the well-known R package glmmLasso through the analyses of both simulated and real-world applications. The simulations and applications demonstrated good properties, such as consistency, and the effectiveness of the proposed variable selection procedure, for both $p < n$ and $p > n$. Moreover, in all evaluated scenarios, the EMLMLasso algorithm outperformed glmmLasso. The proposed method is quite general and can be easily extended for ridge and elastic net penalties in linear mixed-effects models.

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Daniela C. R. Oliveira, Fernanda L. Schumacher, Victor H. Lachos. 2023-08-03. The use of the EM algorithm for regularization problems in high-dimensional linear mixed-effects models. https://arxiv.org/abs/2308.01518

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