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

报告人：王兆军 教授

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.

报告时间：4月18日10:30-11:30

报告地点：统计学院213会议室

主办单位：统计学院

报告人简介：南开大学统计研究院副院长、教授、博导，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、中国现场统计研究会副理事长、中国现场统计研究会生存分析分会副理事长、中国工业统计学教学研究会副理事长、中国统计教育学会高等教育分会副会长、天津市现场统计研究会理事长、天津市统计学会副会长。曾获全国百篇优博指导教师、教育部全国高校自然科学二等奖及天津市自然科学一等奖。目前为《数理统计与管理》副主编，《数学进展》和《统计信息论坛》编委，研究领域包括工业工程中统计监控与诊断、复杂数据中的变点、异常点检测、实验设计、高维数据统计推断等。目前主持国家自然科学基金重点项目和面上项目各一项，已完成多项国家面上项目。已在Journal of the American Statistical Association、Annals of Statistics、Biometrika、Statistica Sinica、Journal of Quality Technology、Journal of Multivariate Analysis、Technometrics、Test等专业顶级期刊上发表数十篇专业学术论文。