【专家简介】：吴月华，加拿大约克大学数学与统计系教授。她师从世界著名统计学家C.R.Rao，于1989年获得美国匹兹堡大学统计学博士学位。目前，她从事高维数据分析、模型选择、变点分析、时空建模、环境统计、统计金融和贝叶斯方法等多领域研究，是国际统计学会的当选会员。她在PNAS，Statistics Sinica, Biometrika, Journal of Economics等期刊上发表了150余篇学术论文，也一直承担加拿大国家自然科学基金科研项目。另外，她目前是Entropy Section Board Member编辑委员会副主编，Statsitical Theory and Related Fields副主编。
【报告摘要】：In this talk, we first briefly review change-point detection methods. Since Change-point detection (CPD) remains a challenging task for complex data characterized by high dimensionality, correlations, outliers, or heavy-tailed distributions. We present an integrated change-point detection method called PCA-uCPD, which utilizes principal component analysis (PCA) to project the original data series into uncorrelated principal components (PCs). We then apply existing univariate change-point detection methods to these PCs, followed by a refining technique to obtain the final change-point estimates for the original sequences. This method features a flexible architecture capable of handling complex data. We provide theoretical justifications to guarantee the feasibility of the method under specific conditions and conduct simulations to assess its performance across various data-generating scenarios. Finally, the efficacy of PCA-uCPD is demonstrated through applications to both genetic and financial datasets.
【报告时间】：2026年06月11日（周四）15:30-16:30
【报告地点】：位育楼417
