题目：Nonlinear Dependence Metrics and Their Statistical Applications

主讲人：伊利诺伊大学厄巴纳-香槟分校 邵晓峰教授

主持人：西南财经大学统计学院 常晋源教授

时间：2024年6月26 - 28日 上午8:30-12:00

地点：西南财经大学光华校区光华裙楼2303

报告摘要：

In these talks, we aim to provide an introduction of nonlinear dependence metrics and their statistical applications. Emphasis will be placed on distance covariance, energy distance and their variants, including Hilbert-Schmidt Independence Criterion, maximum mean discrepancy, martingale difference divergence, among others. The usefulness of these metrics will be demonstrated in some contemporary problems in statistics, such as dependence testing and variable screening/selection for high-dimensional data, as well as dimension reduction and diagnostic checking for multivariate time series. Some recent work on their applications to the inference of non-Euclidean data will also be discussed. The presentations are based on the research results I have obtained in the past and will cover methodology, theory and practical data examples.

主讲人简介：

Xiaofeng Shao received his PhD in Statistics from the University of Chicago in 2006 and has since been a faculty member with the Department of Statistics at the University of Illinois Urbana-Champaign. His current research interests include time series analysis, change-point analysis, functional data analysis, high dimensional data analysis and their applications. He is a fellow of Institute of Mathematical Statistics (IMS) and American Statistical Association (ASA). He currently serves as an associate editor for Journal of Royal Statistical Society, Series B and Journal of Time Series Analysis.