Change point estimation for high-dimensional data

时间:2022-12-29         阅读:


主题Change point estimation for high-dimensional data

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1月6日 9:30-10:30


主办单位:数据科学与商业智能联合实验室 统计学院 科研处


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.



In this talk, I will present some recent work on change point estimation and inference for the location of a change point in the mean of independent high-dimensional data. Our change point location estimator maximizes a new U-statistic based objective function, and its convergence rate and asymptotic distribution after suitable centering and normalization are obtained under mild assumptions. Our estimator turns out to have better efficiency as compared to the least squares based counterpart in the literature. Based on the asymptotic theory, we construct a confidence interval by plugging in consistent estimates of several quantities in the normalization. We also provide a bootstrap-based confidence interval and state its asymptotic validity under suitable conditions. Through simulation studies, we demonstrate favorable finite sample performance of the new change point location estimator as compared to its least squares based counterpart, and our bootstrap-based confidence intervals, as compared to several existing competitors.