题目：Inference for change points in high dimensional data via self-normalization

主讲人：伊利诺伊大学厄巴纳-香槟分校统计系 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：2019年6月21日（星期五）15:00-16:00

地点：西南财经大学光华校区光华楼1007会议室

报告摘要：

In this talk, I will present some recent work on change point testing and estimation for high dimensional data. In the case of testing for a mean shift, we propose a new test which is based on U-statistics and utilizes the self-normalization principle. Our test targets dense alternatives in the high dimensional setting and involves no tuning parameters. We show the weak convergence of a sequential U-statistic based process to derive the pivotal limit under the null and also obtain the asymptotic power under the local alternatives. In addition, we illustrate how our approach can be used in combination with wild binary segmentation to estimate the number and of multiple unknown change points.

主讲人简介：

Xiaofeng Shao is a Professor in the Department of Statistics at University of Illinois at Urbana-Champaign. He completed his undergraduate studies at Nanjing University in 2001 and received PhD in statistics at University of Chicago in 2006. His main research interests includes big data analytics, econometrics, functional data analysis, high dimensional data analysis, spatio-temporal data analysis, time series analysis. Prof. Shao currently serves as the Associate Editor of Journal of the American Statistical Association、Journal of Time Series Analysis and Journal of Multivariate Analysis.