A Relaxation Approach to Feature Selection for Linear Mixed Effects Models
A Relaxation Approach to Feature Selection for Linear Mixed Effects Models
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DOI：
10.1080/10618600.2023.2231496
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发表时间：
2022-05
影响因子：
2.4
通讯作者：
A. Sholokhov;J. Burke;D. Santomauro;P. Zheng;A. Aravkin
中科院分区：
文献类型：
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作者：
A. Sholokhov;J. Burke;D. Santomauro;P. Zheng;A. Aravkin
摘要 线性混合效应 (LME) 模型是对相关数据进行建模的基本工具，包括队列研究、纵向数据分析和荟萃分析。 LME 变量选择方法的设计和分析比线性回归更困难，因为 LME 模型是非线性的。在本文中，我们提出了一种新颖的优化策略，该策略使用凸和非凸正则化器为 LME 提供广泛的变量选择方法，包括 l1、Adaptive-l1、SCAD 和 l0。计算框架只需要每个正则化器的近端算子易于计算，并且可以在开源 python 包 pysr3 中实现，与 sklearn 标准一致。模拟数据集的数值结果表明，所提出的策略在准确性和计算时间方面均优于现有技术。变量选择技术还通过使用欺凌受害数据集的真实示例进行了验证。本文的补充材料可在线获取。
Abstract Linear Mixed-Effects (LME) models are a fundamental tool for modeling correlated data, including cohort studies, longitudinal data analysis, and meta-analysis. Design and analysis of variable selection methods for LMEs is more difficult than for linear regression because LME models are nonlinear. In this article we propose a novel optimization strategy that enables a wide range of variable selection methods for LMEs using both convex and nonconvex regularizers, including l1, Adaptive-l1, SCAD, and l0. The computational framework only requires the proximal operator for each regularizer to be readily computable, and the implementation is available in an open source python package pysr3, consistent with the sklearn standard. The numerical results on simulated data sets indicate that the proposed strategy improves on the state of the art for both accuracy and compute time. The variable selection techniques are also validated on a real example using a data set on bullying victimization. Supplementary materials for this article are available online.