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304am永利集团、所2023年系列学术活动(第029场):Zhou Zhou 教授 多伦多大学

发表于: 2023-04-27   点击: 

报告题目: AUTO-REGRESSIVE APPROXIMATIONS TO NON-STATIONARY TIME SERIES, WITH INFERENCE AND APPLICATIONS

报 告 人: Zhou Zhou 教授 The Department of Statistical Sciences, University of Toronto.

报告时间: 2023年5月9日 15:30-16:30

报告地点: 数学楼第二报告厅

校内联系人:韩月才 hanyc@jlu.edu.cn


报告摘要:Understanding the time-varying structure of complex temporal systems is one of the main challenges of modern time series analysis. In this talk, I will demonstrate that a wide range of short-range dependent non-stationary and nonlinear time series can be well approximated globally by a white-noise-driven auto-regressive (AR) process of slowly diverging order. Uniform statistical inference of the latter AR structure will be discussed through a class of high dimensional L2 tests. I will further discuss applications of the AR approximation theory to globally optimal short-term forecasting, efficient estimation, and resampling inference under complex temporal dynamics.


报告人简介: Zhou Zhou obtained his Ph.D. in Statistics from the University of Chicago in 2009. He is currently a Full Professor at the Department of Statistical Sciences, University of Toronto. Zhou's major research interests lie in complex time series analysis, non- and semi- parametric inference, time-frequency analysis, change point analysis and functional and longitudinal data analysis.