时间：2022.4.1 20:00-21:00
地点：腾讯会议 383 190 897
题目：Change-point detection for COVID-19 time series via self-normalization
报告人：邵晓峰 伊利诺伊大学香槟分校 教授
主持人：於州 华东师范大学教授
摘要：This talk consists of twoparts. In the first part, I will review some basic idea of self-normalization(SN) for inference of time series in the context of confidence intervalconstruction and change-point testing in mean. In the second part, I willpresent a piecewise linear quantile trend model to model infection trajectoriesof COVID-19 daily new cases. To estimate the change-points in the linear trend,we develop a new segmentation algorithm based on SN test statistics and localscanning. Data analysis for COVID-19 infection trends in many countriesdemonstrates the usefulness of our new model and segmentation method.
报告人简介：Dr. Shao is Professor ofStatistics and PhD program director, at the Department of Statistics,University of Illinois at Urbana-Champaign (UIUC). He received his PhD inStatistics from University of Chicago in 2006 and has been on the UIUC facultysince then. Dr. Shao's research interests include time series analysis,high-dimensional data analysis, functional data analysis, change-pointanalysis, resampling methods and asymptotic theory. He is an elected ASA andIMS fellow.