关于举行新加坡南洋理工大学潘光明教授学术报告的通知

报告题目：AUniversal High-Dimensional Data Structural Detection Approach via theLargest Eigenvalue
报告人：潘光明 教授 (新加坡南洋理工大学)
报告时间：2016年7月5日（星期二）上午09:30-10:30
报告地点：4号楼4318室

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                                                                     数学学院
                                                                  2016年07月01日
报告摘要：
 In this talk, we propose to deal with the high-dimensional changepoint detection problem from a new perspective–via the largesteigenvalue. The data dimension p diverges with the sample size n andcan be larger than n. Without any specific parametric distributionassumptions and without any estimators, an optimization approach isproposed to figure out both the unknown number of change points andmultiple change point positions simultaneously. What’s more, anadjustment term is introduced to handle sparse signals when thechange only appears in few components out of the p dimension. Thecomputation time is controlled at O(n^2) by adopting a dynamicprogramming, regardless of the true number of change points k0.Theoretical results are developed and various simulations areconducted to show the effectiveness of our method. Moreover, asapplications, we discuss how to apply the idea proposed in this paperto some other high-dimensional data structure detection problems,e.g. equivalence testing of mean vectors and covariance matrices,which shows the universality of the proposed approach.