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题目：Statistical inference of dynamical regime changes in stochastic sequences in metric spaces

报告人：王学钦 讲席教授（中国科学技术大学）

摘要：With the proliferation of data throughout many fields comes the challenge of detecting abrupt changes in time series that feature complex measured variables that may not be Euclidean in nature. We introduce a method for consistently estimating the number of change-points and identifying their locations in time series that are valued in Metric spaces. We further demonstrate that the estimated change points converge at an optimal rate of O_P (1/T). Analysis of a real dataset and extensive simulations show that our method outperforms state-of-the-art methods, particularly when data are non-Euclidean or covariance structures vary over time.

个人简介：王学钦，中国科学技术大学管理学院讲席教授，2003年毕业于纽约州立大学宾汉姆顿分校，教育部高层次人才入选者。现担任教育部高等学校统计学类专业教学指导委员会委员、中国现场统计研究会副理事长、统计学国际期刊JASA等的Associate Editor、高等教育出版社Lecture Notes: Data Science, Statistics and Probability系列丛书的副主编。

报告时间：2022年11月17日（周四）上午 10：00

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