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新加坡国立大学夏应存教授:Jackknife Approach to The Estimation of Mutual Information

发布时间:2018-12-19

光华讲坛——社会名流与企业家论坛第 5188期

主题:Jackknife Approach to The Estimation of Mutual Information

主讲人:新加坡国立大学夏应存教授

主持人:统计学院 林华珍教授

时间:2018年12月20日(星期四)下午2:00-3:00

地点:西南财经大学柳林校区弘远楼408会议室

主办单位:统计研究中心 统计学院 科研处


主讲人简介:

Prof. Yingcun Xia, received Ph. D. in Statistics from University of Hong Kong in 1999. He worked as a research associate at London school of Economics and Politics and the University of Cambridge from 2000 to 2003. He has been working in Statistics and Applied Probability at National University of Singapore since 2003 and promoted to professor in 2009.Now he is the associate editor of Annals of Statistics, Computational Statistics and Data Analysis, and Computational Statistics.His research interests include high dimensional data analysis, econometric models and risk management, and statistical modelling of infectious diseases, among others.

主要内容:

Quantifying the dependence between two random variables is a fundamental issue in data analysis, and thus many measures have been proposed. Recent studies have focused on the renowned mutual information (MI) [Reshef DN, et al. (2011)]. However, “Unfortunately, reliably estimating mutual information from finite continuous data remains a significant and unresolved problem” [Kinney JB, Atwal GS (2014)]. In this paper, we examine the kernel estimation of MI and show that the bandwidths involved should be equalized. We consider a jackknife version of the kernel estimate with equalized bandwidth and allow the bandwidth to vary over an interval. We estimate the MI by the largest value among these kernel estimates and establish the associated theoretical underpinnings.