Class-Specific Joint Feature Screening in Ultrahigh-Dimensional Mixture Regression

• 成果类型: Article; Early Access
• 署名作者: Jing, Kaili; Khalili, Abbas; Xu, Chen
• 署名单位: Xi'an Jiaotong University; McGill University; Peng Cheng Laboratory
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2025.2468011
• 发表日期: 2025
• 关键词: finite mixture; variable selection; linear-models; algorithm

摘要

Finite mixture of regression models are ubiquitous for analyzing complex data. They aim to detect heterogeneity in the effects of a set of features on a response over a finite number of latent classes. When the number of features is large, a direct fitting of mixture regressions can be computationally infeasible and often leads to a poor interpretative value. One practical strategy is to screen out most irrelevant features before an in-depth analysis. In this article, we propose a novel method for feature screening in ultrahigh-dimensional Gaussian finite mixture of regressions. The new method is built upon a sparsity-restricted expectation-approximation-maximization algorithm, which simultaneously removes varying sets of irrelevant features from multiple latent classes. In the screening process, joint effects between features are naturally accounted and class-specific screening results are produced without ad hoc steps. These merits give the new method an edge to outperform the existing screening methods. The promising performance of the method is supported by both theory and numerical examples including a real data analysis. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.