题目：Self-normalization for the inference of time series

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6月19日（周一）上午8:30-12:00

            2023年6月20日（周二）上午8:30-12:00

            2023年6月21日（周三）上午8:30-12:00

地点：西南财经大学光华校区光华楼10楼1003

报告摘要：

In these talks, we aim to provide a comprehensive introduction of a novel inference technique: the so-called self-normalization, for the analysis of time series data. We plan to cover the use of self-normalization for both confidence interval construction and hypothesis testing in the setting of stationary multivariate time series, functional time series, and high-dimensional time series. Change-point testing and estimation based on self-normalization will also be introduced in detail for both low and high-dimensional data. These talks assume that the teacher or student has the basic background of time series analysis and some research experience in time series analysis is desired but not a prerequisite. The main lecture notes 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.