On Functional Processes with Multiple Discontinuities
报告人：Li Jialiang（ National University of Singapore ）
时间：2022-04-07 14:00-15:30
地点：Tencent Meeting（910 310 323）
Abstract:
We consider the problem of estimating multiple change points for a functional data process. There are numerous examples in science and finance in which the process of interest may be subject to some sudden changes in the mean. The process data that are not in a close vicinity of any change point can be analysed by the usual nonparametric smoothing methods. However, the data close to change points and contain the most pertinent information of structural breaks need to be handledwith special care. This paper considers a half-kernel approach that addresses the inference of the total number, locations and jump sizes of the changes. Convergence rates and asymptotic distributional results for the proposed procedures are thoroughly investigated. Simulations are conducted to examine the performance of the approach, and a number of real data sets are analysed to provide an illustration.
Biography:
栗家量，新加坡国立大学统计与应用概率系教授，同时在杜克大学-新加坡国大医学院与新加坡眼科研究所任兼职教授。栗教授于2001年中国科学技术大学获得统计学学士学位，分别于2005年和2006年在美国威斯康星大学麦迪逊分校获得公共健康学硕士学位和统计学博士学位。现在研究兴趣包括工具变量、子集分析、变点模型、结构方程、精准医学、诊断医学、 模型平均、非参、生存分析等。已发表论文160余篇，他是ASA的Fellow和ISI的Elected Member。
Tencent Meeting：https://meeting.tencent.com/dm/CKNRtMFz0Txn
Meeting ID：910 310 323
