Dear Colleagues,
This is a reminder that the ASA SLDS October webinar is on this Tuesday. Professor Runze Li from Penn State University will discuss about high-dimensional statistical inference. Hope to see you there!
Title: High-Dimensional Statistical Inference
Speakers: Prof. Runze Li, Department of Statistics, Pennsylvania State University
Date and Time: October 29, 2024, 2:00 to 3:30 pm Eastern Time
Registration Link: ASA SLDS Webinar Registration Link [eventbrite.com]
Abstract: Statistical inference for high-dimensional means and regression coefficients has received a lot of attention in recent literature. In this talk, I will first present an overview of tests on high-dimensional mean problems and statistical inference procedures for high-dimensional regression coefficients. I will then introduce a new test procedure for high dimensional linear hypothesis problems. Through a projection approach that aims to separate useful inferential information from the nuisance one, our proposed test accurately accounts for the impact of high-dimensional nuisance parameters. We discover that with a carefully designed projection matrix, the projection procedure enables us to transform the problem of interest into a test on moment conditions, from which we construct a U-statistic-based test that is applicable in simultaneous inference on a diverging number of linear hypotheses. We prove that under regularity conditions, the plug-in test statistic converges to its oracle counterpart, acting as well as if the nuisance parameters were known in advance. Moreover, we introduce an implementation-friendly version to tackle the computational challenge. Asymptotic null normality is established to provide convenient tools for statistical inference, accompanied by rigorous power analysis. To further strengthen the testing power, we develop two power enhancement techniques to boost the power from two distinct aspects respectively and integrate them into one powerful testing procedure to achieve double power enhancement. The finite-sample performance is demonstrated using simulation studies, and an empirical analysis of a real data example.
Presenter: 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.
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Zhihua Su, PhD
Associate Professor
Department of Statistics
University of Florida
zhihuasu@stat.ufl.edu------------------------------