The paper “Statistical Inference for Mediation Models with High Dimensional Exposures and Mediators,” co-authored by Dr. Wei Zhou, Prof. Jingyuan Liu and doctoral candidate Xinyu Zhang of Xiamen University, and Prof. Jian Kang of the University of Michigan, has been officially accepted by the Journal of the American Statistical Association, one of the leading international journals in the field of statistics.
Abstract
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.
Author Introduction
Xinyu Zhang is a doctoral candidate at the Zou Zhizhuang Institute of Economics, Xiamen University. Her research primarily focuses on mediation analysis and causal inference.
Wei Zhou is a Lecturer at the Joint Laboratory of Data Science and Business Intelligence, Southwestern University of Finance and Economics. His research primarily focuses on causal inference, graphical models, and high-dimensional statistics.
Jingyuan Liu is a Professor at the School of Economics and the Wang Yanan Institute for Studies in Economics, Xiamen University. Her research primarily focuses on ultra-high dimensional and complex data modeling, mediation effects and causal inference, statistical modeling empowered by large models, and interdisciplinary statistical methodologies.
Jian Kang is a Professor in the Department of Biostatistics at the University of Michigan. His research primarily focuses on statistical methods for large-scale and complex biological data.
