报告主题：Multiple Change Point Detection for Correlated High-Dimensional Observations via the Largest Eigenvalue
报告人： Guangming Pan 教授 （Nanyang Technological University, Singapore）
报告时间：2017年 11月15日（周三）17:00
报告地点：校本部G507
邀请人：王卿文
报告摘要：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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