Individualized Multidirectional Variable Selection

• 成果类型: Article
• 署名作者: Tang, Xiwei; Xue, Fei; Qu, Annie
• 署名单位: University of Virginia; University of Pennsylvania
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1705308
• 发表日期: 2021
• 页码: 1280-1296
• 关键词: GENERALIZED ESTIMATING EQUATIONS; REGRESSION SHRINKAGE; subgroup analyses; cluster-analysis; drug-dosage; regularization; number; medicine

摘要

In this article, we propose a heterogeneous modeling framework which achieves individual-wise feature selection and heterogeneous covariates' effects subgrouping simultaneously. In contrast to conventional model selection approaches, the new approach constructs a separation penalty with multidirectional shrinkages, which facilitates individualized modeling to distinguish strong signals from noisy ones and selects different relevant variables for different individuals. Meanwhile, the proposed model identifies subgroups among which individuals share similar covariates' effects, and thus improves individualized estimation efficiency and feature selection accuracy. Moreover, the proposed model also incorporates within-individual correlation for longitudinal data to gain extra efficiency. We provide a general theoretical foundation under a double-divergence modeling framework where the number of individuals and the number of individual-wise measurements can both diverge, which enables inference on both an individual level and a population level. In particular, we establish a strong oracle property for the individualized estimator to ensure its optimal large sample property under various conditions. An efficient ADMM algorithm is developed for computational scalability. Simulation studies and applications to post-trauma mental disorder analysis with genetic variation and an HIV longitudinal treatment study are illustrated to compare the new approach to existing methods. for this article are available online.