简介：周望，新加坡国立大学（National University of Singapore）应用统计系教授，2004年获得香港科技大学统计学博士学位。周望教授的研究领域包括高维数据估计与检验、高维数据降维、SLE（Schramm–Loewner Evolution）、高维随机矩阵、多元数据分析等。截止目前，周望教授共发表70余篇研究论文，仅在概率统计、经济、信息论领域顶级期刊Journal of Econometric、Econometric Theory、Annals of Statistics、Journal of the American Statistical Association、Journal of the Royal Statistical Society, Series B、Annals of Prob.、Annals of Applied Prob.、Biometrika、IEEE Transactions on Information Theory期刊上共发表二十余篇。目前，周望教授担任SCI期刊《随机矩阵：理论与应用》的主编工作。
报告题目：High order conditional distance covariance with conditional mutual independence
教授观点：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 PM2.5 in five Chinese cities: Beijing, Tianjin, Jinan, Tangshan and Qinhuangdao by Gaussian graphical model.
