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学术报告五十四:Multiple change-points detection in high dimension

来源:数学与统计学院     作者:     时间:2018/6/22 9:20:49  0次

数学与统计学院学术报告[2018] 054

(高水平大学建设系列报告173)

报告题目:  Multiple change-points detection in high dimension

报告人:  王兆军教授 (南开大学

报告时间:2018625日下午400500

报告地点: 科技楼501

邀请人:    魏正红

报告摘要:

Change-point detection is an integral component of statistical modeling and estimation. For high-dimensional data, classical methods based on the Mahalanobis distance are typically inapplicable. We propose a novel testing statistic by combining a modified Euclidean distance and an extreme statistic, and its null distribution is asymptotically normal. The new method naturally strikes a balance between the detection abilities for both dense and sparse changes, which gives itself an edge to potentially outperform existing methods. Furthermore, the number of change-points is determined by a new Schwarz’s information criterion together with a pre-screening procedure, and the locations of the change-points can be estimated via the dynamic programming algorithm in conjunction with the intrinsic order structure of the objective function. Under some mild conditions, we show that the new method provides consistent estimation with an almost optimal rate. Simulation studies show that the proposed method has satisfactory

performance of identifying multiple change-points in terms of power and estimation accuracy, and two real data examples are used for illustration.

报告人简介:

南开大学统计研究院教授 教育部长江学者特聘教授 国务院学位委员会统计学科评议组成员 国家统计咨询委员会委员 中国现场统计研究会副理事长  天津数据科学与技术学会理事长 曾获全国百篇优博指导教师及天津市自然科学一等奖

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