报告题目：Testing for Change-points in Heavy-tailed Time Series---A Winsorized CUSUM Approach
报 告 人：凌仕卿 教授 香港科技大学
报告时间：2024年6月7日15:00-16:00
报告地点：#腾讯会议：923-255-324
校内联系人：朱复康 fzhu@jlu.edu.cn
报告摘要：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)
报告人简介：凌仕卿，香港科技大学数学系讲座教授，数理统计学会会士(Fellow of IMS)与计量经济期刊会士(Fellow of JOE)，澳大利亚与纽西兰模型与模拟学会会士(Fellow of MSSANZ)并荣获该学会2013双年度勋章，荣获 Econometric Theory Plura Scripsit奖。凌教授是香港研资局高级研究学者，目前正担任《Journal of Time Series Analysis》联合主编，以及《Statistica Sinica》，《计量经济学报》与其他三个期刊的副主编。凌教授主要研究领域是时间序列分析与计量经济学，他有三项原创性贡献，包括提出一个向量ARMA-GARCH 模型，提出以残差为基础的二次型统计量与提出一个自加权估计方法；他在变点问题、GARCH-类模型、门限模型与单位根问题方面都有非常重要的基础性贡献。
