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Multiple Change Point Detection for Correlated High-Dimensional Observations via the Largest Eigenvalue
时间  Datetime
2017-11-16 15:00 — 16:00 
地点  Venue
Middle Lecture Room
报告人  Speaker
Prof. Guangming Pan
单位  Affiliation
Nanyang Technological University, Singapore
邀请人  Host
Cheng Wang
报告摘要  Abstract

Abstract: We propose to deal with a mean vector change point detection problem from a new perspective via the largest eigenvalue when the data dimension p is comparable to the sample size n. An optimization approach is proposed to figure out both the unknown number of change points and multiple change point positions simultaneously. Moreover, an adjustment term is introduced to handle sparse signals when the change only appears in few components out of the p dimensions. The computation time is controlled at $O(n^2)$ by adopting a dynamic programming, regardless of the true number of change points $k_0$. Theoretical results are developed and various simulations are conducted to show the effectiveness of our method.