题目：Segmenting time series via self-normalization

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：2022年3月25日（周五）上午9:30-10:30

地点：Zoom，884 5772 1268

报告摘要：

We propose a novel and unified framework for change-point estimation in multivariate time series. The proposed method is fully nonparametric, tuning-free and robust to temporal dependence. Moreover, it treats change-point detection for a broad class of parameters (such as mean, variance, correlation and quantile) in a unified fashion. At the core of our method, we couple the self-normalization (SN) based tests with a novel nested local-window segmentation algorithm, which seems new in the growing literature of change-point analysis. Due to the presence of an inconsistent long-run variance estimator in the SN test, non-standard theoretical arguments are further developed to derive the consistency and convergence rate of the SN-based change-point detection method. Extensive numerical experiments and relevant real data analysis are conducted to illustrate the effectiveness and broad applicability of our proposed method in comparison with state-of-the-art approaches in the literature.)

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.