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Lecture on "High Order Conditional Distance Covariance with Conditional Mutual Independence"
Date and Time: 2020-10-14 09:19:52

Speaker: Zhou Wang, Professor, Department of Statistics and Applied Probability, National University of Singapore

Date: Oct. 14, 2020

Time: 10:00 a.m.

Location: Tencent Meeting, ID: 405 635 035

Link: https://meeting.tencent.com/s/wqR3HE2YaWgI

Sponsor: Zhongtai Securities Institute for Financial Studies

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.

Bio:

Prof. Zhou Wang is currently professor of Statistics and Applied Probability department at National University of Singapore. His main research interests include the Theory and Application of Statistics and he has made important achievement on High-dimensional data estimation, high-dimensional data inspection, data dimensionality reduction and large-dimensional data random matrix. He has published in top journals such as 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 and Trans. Amer. Math. Soc..

For more information, please visit:

http://mathfinance.sdu.edu.cn/info/1273/4722.htm

Edited by: Wei Zhen, Xie Tingting




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