题目：Statistical Inference for Time Series via Sample Splitting

主讲人：伊利诺伊大学厄巴纳-香槟分校 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：2024年7月1日（周一）上午08:30-12:00

    2024年7月1日（周一）下午14:30-16:00

    2024年7月2日（周二）上午08:30-12:00

地点：西南财经大学光华校区光华裙楼2303

报告摘要：

Sample splitting has found widespread application in numerous contemporary statistical problems, such as post-selection inference, conformal prediction, and high-dimensional inference. Its effectiveness often relies on the assumption of independent and identically distributed (iid) data generation processes, yet there has been limited exploration into its suitability for handling dependent data. In these talks, we introduce a novel approach to statistical inference for time series data, which integrates sample splitting and self-normalization. We illustrate this new methodology for several problems: dimension-agnostic change point testing for multivariate time series, change-point detection for object-valued time series and one-sample and two-sample testing for a functional parameter in Hilbert-space-valued time series. We will present both asymptotic theory and numerical results to demonstrate its broad applicability in inferring parameters of low, high, and infinite dimensions.

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.