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Seminar No.1532 Multiple Change Point Detection for Correlated High-Dimensional Observations via the Largest Eigenvalue

Created Date 11/15/2017 福平   View Numbers  122 Return    
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Title: Multiple Change Point Detection for Correlated High-Dimensional Observations via the Largest Eigenvalue
Reporter: Prof. Guangming Pan  (Nanyang Technological University, Singapore)
Time: 2017-11-15 (Wednesday) 17:00
Place: G507

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.


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