Statistical Inference for Mediation Models with High Dimensional Exposures and Mediators
本文提出部分正则化推断方法（PRIME），用于高维暴露和中介变量同时存在时的中介效应分析，通过分组部分惩罚最小二乘法估计直接和间接效应，并构建F检验和Wald检验，应用于阿尔茨海默病遗传变异与脑活动变化的中介效应研究。
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 paper 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.