题目：Model-free variable selection in sufficient dimension reduction via FDR control

摘要：Simultaneously identifying contributory variables and controlling the false discovery rate (FDR) in high-dimensional data is an important statistical problem. In this paper, we propose a novel model-free variable selection procedure in sufficient dimension reduction via data splitting technique. The variable selection problem is first connected with a least square procedure with several response transformations. We construct a series of statistics with global symmetry property and then utilize the symmetry to derive a data-driven threshold to achieve error rate control. This method can achieve finite-sample and asymptotic FDR control under some mild conditions. Numerical experiments indicate that our procedure has satisfactory FDR control and higher power compared with existing methods.

简介：Yixin Han, postdoctoral at Information Systems, Business Statistics and Operations Management, the Hong Kong University of Science and Technology. She received her Ph.D. degree in Statistics from the School of Statistics and Data Science at Nankai University in 2021 supervised by Prof. Zhaojun Wang and Prof. Changliang Zou. Her research interests include optimal subsampling in large-scale data, high-dimensional data inference, semi-supervised learning, and multiple testing related topics.

照片：

报告人：韩艺欣

邀请人：冶继民

报告平台：腾讯会议ID：393-466-043

时间：2022-11-23，15：00-16：45