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.

翻译：EM算法是极大似然估计的常用工具，但在线性混合效应模型的高维正则化问题中尚未得到广泛应用。本文提出EMLMLasso算法，该算法结合EM算法与高效流行的R包glmnet，用于线性混合效应模型中固定效应的Lasso变量选择。通过模拟和实际数据分析，我们比较了所提出的EMLMLasso算法与知名R包glmmLasso中实现算法的性能。模拟与实际应用表明，所提出的变量选择程序在$p < n$和$p > n$两种情形下均具有良好的相合性等特性与有效性。此外，在所有评估场景中，EMLMLasso算法的表现均优于glmmLasso。所提方法具有较强通用性，可便捷地推广至线性混合效应模型中的岭回归与弹性网惩罚项。