报告题目：High order conditional distance covariance with conditional mutual independence

主 讲 人：周望教授

报告时间：2020年10月14日10:00-11:00

报告地点：腾讯会议 会议 ID：405 635 035

点击链接入会： https://meeting.tencent.com/s/wqR3HE2YaWgI

报告摘要：

We construct a high order conditional distance covariance, which generalizes the notation of conditional distance covariance. The joint conditional distance covariance is defined as a linear combination of conditional distance covariances, which can capture the joint relation of many random vectors given one vector. Furthermore, we develop a new method of conditional independent test based on the joint conditional distance covariance. Simulation results indicate that the proposed method is very effective. We also apply our method to analyze the relationships of PM 2.5 in five Chinese cities: Beijing, Tianjin, Jinan, Tangshan and Qinhuangdao by Gaussian graphical model.

主讲人简介：

周望，新加坡国立大学统计与应用概率系教授。主要从事统计学的理论与应用研究，在高维数据估计、高维数据检验、数据降维、大维数据随机矩阵领域取得了重要的成果。迄今为止，在Annals of Probability，Annals of Applied Probability，Annals of Statistics, Journal of American Statistical Association, Journal of Royal Statistical Society（B）, Biometrika, Bernoulli, Journal of Econometrics，Trans. Amer. Math. Soc. 等国际顶级期刊发表论文近60篇。

邀请人：何勇

欢迎各位老师同学积极参加！