论文标题
分析混合效应模型中特征选择的松弛方法
Analysis of Relaxation Methods for Feature Selection in Mixed Effects Models
论文作者
论文摘要
线性混合效应（LME）模型是建模聚类数据的基本工具，包括队列研究，纵向数据分析和荟萃分析。 LME的可变选择方法的设计和分析要比线性回归要困难得多，因为LME模型是非线性的。此处考虑的方法是由最新的稀疏放松正则回归（SR3）的方法在线性回归背景下进行的。开发了提议扩展到LME的理论基础，包括一致性结果，变异性质，优化方法的可实施性和收敛结果。特别是，我们为LME（称为MSR3）和加速的混合算法（称为MSR3-FAST）的SR3基本实现提供了收敛分析。数值结果显示了这些算法在逼真的模拟数据集上的效用和速度。数值实现可在开源Python软件包PYSR3中获得。
Linear Mixed-Effects (LME) models are a fundamental tool for modeling clustered data, including cohort studies, longitudinal data analysis, and meta-analysis. The design and analysis of variable selection methods for LMEs is considerably more difficult than for linear regression because LME models are nonlinear. The approach considered here is motivated by a recent method for sparse relaxed regularized regression (SR3) for variable selection in the context of linear regression. The theoretical underpinnings for the proposed extension to LMEs are developed, including consistency results, variational properties, implementability of optimization methods, and convergence results. In particular we provide convergence analyses for a basic implementation of SR3 for LME (called MSR3) and an accelerated hybrid algorithm (called MSR3-fast). Numerical results show the utility and speed of these algorithms on realistic simulated datasets. The numerical implementations are available in an open source python package pysr3.
