主题：Jackknife Approach to The Estimation of Mutual Information
主办单位：统计研究中心 统计学院 科研处
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.
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.