报告题目：Multiple Change Points Detection in High-DimensionalMultivariate Regression
报告时间：2021年6月30日 9：00——11：00
报告方式：宝盈娱乐app宁远楼213（线上同步腾讯会议ID 305 6047 5990）
报告内容：We consider the problem of detecting structural changes in a high-dimensional regression setting. The structural parameters are subject to abrupt changes of unknown magnitudes at unknown locations. We propose a new procedure that minimizes a penalized least-squares loss function via a dynamic programming algorithm for estimating the locations of change points. To alleviate the computational burden, we adopt a prescreening procedure by eliminating a large number of irrelevant points before implementing estimation procedure. The number of change points is determined via Schwarz’s information criterion. Under mild assumptions, we establish the consistency of the proposed estimators, and further provide error bounds for estimated parameters which achieve almost-optimal rate. Simulation studies show that the proposed method performs reasonably well in terms of estimation accuracy, and a real data example is used for illustration.
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嘉宾简介：
马晓燕，本科和硕士分别毕业于宝盈娱乐app和武汉大学，现为南开大学统计与数据科学院博士生。主要研究方向是高维数据统计推断、变点和异常值检测。
宝盈娱乐app数学统计学院
宁夏师范学院数学与计算机科学学院
2021年6月28日
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