题目：Dimension-agnostic change point detection

主讲人：伊利诺伊大学香槟分校 邵晓峰教授

主持人：统计学院 常晋源教授

时间：2023年4月14日（周五）上午9:00-10:00

地点：腾讯会议，300 588 622

报告摘要：

The detection and estimation of change-point(s) in the mean is a classical problem in statistics and has broad applications in a wide range of areas. Though many methods have been developed in the literature, most are applicable only under a specific dimensional setting. Specifically, the methods designed for low-dimensional problems may not work well in the high-dimensional environment and vice versa. Motivated by this limitation, we propose a dimension-agnostic procedure of change-point testing for time series by applying dimension reduction and self-normalization. Our test statistics can accommodate both temporal and cross-sectional dependence, regardless of the dimensionality. Both asymptotic theory and numerical studies confirm the appealing property of the proposed test. A review of self-normalization for time series will be provided in the beginning to make the talk self-contained.

主讲人简介：

Dr. Shao is Professor of Statistics and PhD program director, at the Department of Statistics, University of Illinois at Urbana-Champaign (UIUC). He received his PhD in Statistics from University of Chicago in 2006 and has been on the UIUC faculty since then. Dr. Shao's research interests include time series analysis, high-dimensional data analysis, functional data analysis, change-point analysis, resampling methods and asymptotic theory. He is an elected ASA and IMS fellow.