報告題目：Testing for Change-points in Heavy-tailed Time Series---A Winsorized CUSUM Approach
報 告 人：淩仕卿 教授 香港科技大學
報告時間：2024年6月7日15:00-16:00
報告地點：#騰訊會議：923-255-324
校内聯系人：朱複康 fzhu@jlu.edu.cn
報告摘要：It is well-known how to detect the change-point in heavy-tailed time series is an open problem since the traditional tests may not have a power. This article proposes a winsorized CUSUM approach to solve this problem. We begin by investigating the winsorized CUSUM process and deriving the limiting distributions of the Kolmogorov-Smirnov test and the Self-normalized test under the null hypothesis. Under the alternative hypothesis, we firstly uncover the behavior of change-point magnitude after the winsorized data and show that our tests have a power approaching to 1 as the sample size $n\to\infty$. We then extend the winsorizing technique to tests for multiple change-points without the prior information on the number of actual change points. Our framework is quite general and its assumption is very weak. This enables the application of our tests to both linear time series and nonlinear time series, such as TAR and G-GARCH processes. The empirical results illustrate the effectiveness of our proposed procedures for change-point detection. (This is a joint work with Rui She and Linlin Dai)
報告人簡介：淩仕卿，香港科技大學數學系講座教授，數理統計學會會士(Fellow of IMS)與計量經濟期刊會士(Fellow of JOE)，澳大利亞與紐西蘭模型與模拟學會會士(Fellow of MSSANZ)并榮獲該學會2013雙年度勳章，榮獲 Econometric Theory Plura Scripsit獎。淩教授是香港研資局高級研究學者，目前正擔任《Journal of Time Series Analysis》聯合主編，以及《Statistica Sinica》，《計量經濟學報》與其他三個期刊的副主編。淩教授主要研究領域是時間序列分析與計量經濟學，他有三項原創性貢獻，包括提出一個向量ARMA-GARCH 模型，提出以殘差為基礎的二次型統計量與提出一個自加權估計方法；他在變點問題、GARCH-類模型、門限模型與單位根問題方面都有非常重要的基礎性貢獻。
