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    报告题目：Hierarchical Change-Point Detection

    报告摘要：Sequences of random objects arise from many real applications, including high throughput omic data and functional imaging data. Those sequences are usually dependent, non-linear, or even Non-Euclidean, and an important problem is change-point detection in such dependent sequences in Banach spaces or metric spaces. The problem usually requires the accurate inference for not only whether changes might have occurred but also the locations of the changes when they did occur. To this end, we first introduce a Ball detection function and show that it reaches its maximum at the change-point if a sequence has only one change point. Furthermore, we propose a consistent estimator of Ball detection function based on which we develop a hierarchical algorithm to detect all possible change points. We prove that the estimated change-point locations are consistent. Our procedure can estimate the number of change-points and detect their locations without assuming any particular types of change-points as a change can occur in a sequence in different ways. Extensive simulation studies and analyses of two interesting real datasets wind direction and Bitcoin price demonstrate that our method has considerable advantages over existing competitors, especially when data are non-Euclidean or when there are distributional changes in the variance.

    报告人简介：王学钦，中国科学技术大学管理公司教授。2003年毕业于纽约州立大学宾厄姆顿分校, 2012年入选教育部新世纪优秀人才支持计划学者， 2013年获得国家优秀青年研究基金，2014年入选第八批广东省高等学校“千百十工程”国家级培养计划，2016年入选“广东特支计划”（百千工程领军人才）。此外，他还担任教育部高等学校统计学类专业教学指导委员会委员、统计学国际期刊《JASA》、《SII》、《JCS》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、中国现场统计研究会数据科学与人工智能分会副理事长和中国青年统计学家协会副会长等。

    报告时间：2020年8月8日上午9:30-11:00

    报告地点：腾讯会议（ID：784945443）https://meeting.tencent.com/s/BZzxyzecilSN

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2020年8月6日