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.
