Keynote Speaker:Zhou Wang

Abstract:

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.

Introduction to the Speaker:

Zhou Wang, Professor of the Department of Statistics and Applied Probability at the National University of Singapore. He is mainly engaged in the theoretical and applied research of statistics, and achieved important results in the fields of the estimation of high-dimensional data, inspection of high-dimensional data, data dimensionality reduction and random matrix of large-dimensional data. So far, he has published nearly 60 papers in top international journals such asAnnals of Probability,Annals of Applied Probability,Annals of Statistics,Journal of American Statistical Association,Journal of Royal Statistical Society (B),Biometrika,Bernoulli,Journal of Economics,Trans Amer Math Soc, etc.

Inviter:

He Yong

Time:

10:00-11:00 on October 14 (Wednesday)

Location:

Tencent Meeting, ID: 405 635 035