SDS Colloquium Series

Topic

Model-Free Statistical Inference on High-Dimensional Data

Speaker

Runze LI, Eberly Family Chair Professor, The Pennsylvania State University

Host

Chien-Fu Jeff WU, X.Q. Deng Presidential Professor, School of Data Science, CUHK-Shenzhen

Date

22 November (Friday), 2024

Time

11:00 AM - 12:00 PM, Beijing Time

Format

Onsite

Venue

103 Meeting Room, Daoyuan Building

Language

English

Abstract

My talk aims to introduce an effective model-free inference procedure for high-dimensional data. We first reformulate the hypothesis testing problem via sufficient dimension reduction framework. With the aid of new reformulation, we propose a new test statistic and show that its asymptotic distribution is chi-square distribution whose degree of freedom does not depend on the unknown population distribution. We further conduct power analysis under local alternative hypotheses. In addition, we study how to control the false discovery rate of the proposed chi-square tests, which are correlated, to identify important predictors under a model-free framework. To this end, we propose a multiple testing procedure and establish its theoretical guarantees. Monte Carlo simulation studies are conducted to assess the performance of the proposed tests and an empirical analysis of a real-world data set is used to illustrate the proposed methodology.

Biography

Runze Li is the Eberly Family Chair Professor in Statistics, The Pennsylvania State University. He served as Co-Editor of Annals of Statistics from 2013 to 2015. Runze Li is a Fellow of IMS, ASA and AAAS. His recent honors and awards also include the Distinguished Achievement Award of International Chinese Statistical Association, 2017, Faculty Research Recognition Awards for Outstanding Collaborative Research. College of Medicine, Penn State University in 2018, Distinguished Mentoring Award, Eberly College of Science, Penn State University in 2023, IMS Medallion lecture in 2023 and IMS Carver medal in 2024. His research interests include theory and methodology in variable selection, feature screening, statistical inference for high-dimensional data, robust statistics, nonparametric and semiparameteric regression. His interdisciplinary research aims to promote the better use of statistics in social behavioral research, neural science research and climate studies.