【报告摘要】This talk will survey recent advances in understanding Chatterjee's graph correlation coefficient. I will introduce, for the first time, a comprehensive theoretical framework for statistical inference based on this coefficient. The framework involves results on asymptotic normality, the (in)consistency of bootstrap methods, and bias correction.

【报告人简介】Dr. Fang Han is a full professor and as the Job & Gertrude Tamaki Endowed Professor in Statistics, in economics (by courtesy) at the University of Washington, and an affiliated investigator in Fred Hutchinson Cancer Research Center. He obtained his Ph.D. (Biostatistics) from Johns Hopkins University in 2015. Previously, he received his B.S. (Mathematics) from Peking University and M.S. (Biostatistics) from University of Minnesota. Dr. Han is a current associate editor for Bernoulli and an editorial advisory board member of Dependence Modeling.