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

报告人：周望（新加坡国立大学）

报告校内联系人：鲁大伟  联系电话：0411-84708351

报告地点：创新园大厦A1101

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.

报告人简介：

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，特别在高维数据估计、高维数据检验、数据降维、大维数据随机矩阵领等域取得了重要的成果。迄今为止，在Annals of Statistics, Journal of American Statistical Association, Journal of Royal Statistical Society Series B, Biometrika, Bernoulli, Journal of Econometrics, Transactions of the American Mathematical Society, Annals of Probability, Annals of Applied Probability等国际顶级概率统计期刊上发表论文60余篇。

主要研究方向：随机矩阵理论

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2019年7月17日