题 目：Online Change-Point Detection for Functional Data
主讲人：朱汉兵 副教授
单 位：安徽大学
时 间：2025年11月21日 16:00
地 点：九章学堂南楼C座302
摘 要：We propose a CUSUM-type online change-point detection procedure for monitoring a change in the mean function of dependent functional data. Our method is fully nonparametric and does not require dimension reduction for the functional observations. For the proposed sequential monitoring scheme, we provide the limiting distribution of the CUSUM monitoring statistic under the null hypothesis of no change, which yields the threshold to control the global false alarm rate asymptotically. Furthermore, we show that the proposed sequential test has an asymptotic power one. The method is illustrated by means of Monte Carlo simulation studies and an application to a real dataset.
简 介：朱汉兵，安徽大学大数据与统计学院副教授，硕士研究生导师。主要从事关于函数型数据、空间数据、脑图像数据分析，高维统计，变点分析，非参数统计等的研究工作。在Statistica Sinica、Journal of Computational and Graphical Statistics、Journal of Multivariate Analysis、Computational Statistics & Data Analysis等期刊发表学术论文二十余篇。主持国家自然科学基金和安徽省高校优秀青年科学基金等项目。加州大学河滨分校、香港大学、香港中文大学、香港理工大学等高校的访问学者，中国青年统计学家协会以及中国现场统计研究会若干分会的理事，Journal of the American Statistical Association、Statistica Sinica、Biometrics、Science China Mathematics等期刊的审稿人。
