报告时间：2018年12月17日（周一）上午10：00-11：00
报告地点：理科大楼A楼1716
报告题目：Jackknife approach to the estimation of mutual information
报告人：Yingcun Xia 新加坡国立大学
摘要：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) Science 334:1518–1524]. However, “Unfortunately, reliably estimating mutual information from finite continuous data remains a significant and unresolved problem” [Kinney JB, Atwal GS (2014) Proc Natl Acad Sci USA 111:3354–3359]. 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.
报告人简介：Yingcun Xia教授任教于新加坡国立大学，主要研究领域有降维、金融时间序列分析、非线性依赖性的度量等，已在统计学四大国际顶级期刊Annals of Statistics, JASA, JRSSB, Biometrika上发表20余篇文章，是Annals of Statistics和Computational Statistics and Data Analysis期刊的副主编。