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High dimensional mean change point detection via the largest eigenvalue
澳门新葡8455最新网站:2016年12月21日 13:39 点击数:

报告人:Guangming Pan

报告地点:澳门新葡8455最新网站403室

报告澳门新葡8455最新网站:2016年12月21日星期三15:30-16:30

邀请人:

报告摘要:

He propose to deal with the high-dimensional change point detection problem from a new perspective--via the largest eigenvalue.The data dimension p diverges with the sample size n and can be larger than n.An optimization approach is proposed to figure out both the unknown number of change points and multiple change point positions simultaneously. What's more, 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.

主讲人概况:

Guangming Pan is an Associate Professor of the School of Physical & Mathematical Sciences, and he obtained his PhD in mathematical statistics, University of Science and Technology of China in 2005.At present, He devoted himself to Random matrix theory, high dimensional statistics inference, applications of probability.

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