数学与信息科学学院学术报告(20170613)
发布时间:2017-06-07 浏览次数:
题  目: Multiple Change Point Detection for Correlated High-Dimensional Observations via the Largest Eigenvalue
时  间:2017年6月13(星期二)下午15:00
报告人:潘光明教授、博导(新加坡南洋理工大学)
地  点:先骕楼数信学院四楼3425多媒体报告厅
 
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 
 
报告人简介:
 2002年6月硕士毕业于安徽大学数学科学学院,概率统计专业;2005年7月博士毕业于中国科学技术大学,数理统计专业;之后在新加坡国立大学、台湾国立中山大学、和荷兰埃因霍温科技大学做博士后和学术交流工作。自2008年以来,在新加坡南洋理工大学工作,教授博士生导师。
 主要研究领域:高维统计推断、随机矩阵理论、计量经济学、多元统计、应用概率等。在《Annals of Statistics》、《Annals of Probability》、《Annals of Applied Probability》、《The Journal of the Royal Statistical Society, Series B》等概率与统计顶级期刊上发表20多篇学术论文。