Model-free High Dimensional Mediator Selection with False Discovery Rate Control
Abstract
There is a challenge in selecting high-dimensional mediators when the mediators have complex correlation structures and interactions. In this work, we frame the high-dimensional mediator selection problem into a series of hypothesis tests with composite nulls, and develop a method to control the false discovery rate (FDR) which has mild assumptions on the mediation model. We show the theoretical guarantee that the proposed method and algorithm achieve FDR control. We present extensive simulation results to demonstrate the power and finite sample performance compared with existing methods. Lastly, we demonstrate the method for analyzing the Alzheimer's Disease Neuroimaging Initiative (ADNI) data, in which the proposed method selects the volume of the hippocampus and amygdala, as well as some other important MRI-derived measures as mediators for the relationship between gender and dementia progression.
Cite
@article{arxiv.2505.09105,
title = {Model-free High Dimensional Mediator Selection with False Discovery Rate Control},
author = {Runqiu Wang and Ran Dai and Jieqiong Wang and Kah Meng Soh and Ziyang Xu and Mohamed Azzam and Hongying Dai and Cheng Zheng},
journal= {arXiv preprint arXiv:2505.09105},
year = {2025}
}