Statistical Inference for Mediation Models with High Dimensional Exposures and Mediators

• 成果类型: Article; Early Access
• 署名作者: Zhang, Xinyu; Zhou, Wei; Liu, Jingyuan; Kang, Jian
• 署名单位: Xiamen University; Southwestern University of Finance & Economics - China; Xiamen University; University of Michigan System; University of Michigan
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2026.2621518
• 发表日期: 2026-06-27
• 关键词: High-dimensional mediation analysis; latent factor model; Multiple test; Partially penalized least squares

摘要

High-dimensional mediation analysis has gained increasing interest in various fields, particularly in genetic and medical research. Compared with existing works that focus mainly on high-dimensional mediators, this article advocates a new framework of Partial Regularization-based Inference for Mediation Effects (PRIME) when both exposures and mediators are high-dimensional. Estimated direct and indirect effects are established using a group-wise partially penalized least squares method, incorporating a double-layer latent factor structure. F-type and Wald tests for the high-dimensional direct and indirect effects, respectively, are advocated based on the proposed estimators. Both theoretical and numerical performance of PRIME have been carefully studied. PRIME is also applied to investigating direct effects of genetic variants on Alzheimer's disease (AD) and indirect effects of them mediated by changes in brain activity intensity. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.