Speaker: Qi-Man Shao   Department of Statistics and Data Science Southern University of Science and Technology

Abstract: Distance correlation has become an increasingly popular tool for detecting the nonlinear dependence between a pair of potentially high-dimensional random vectors. Most existing works have explored its asymptotic distributions under the null hypothesis of independence between the two random vectors when only the sample size or the dimensionality diverges. Yet its asymptotic null distribution for the more realistic setting when both sample size and dimensionality diverge in the full range remains largely underdeveloped. In this talk, we will develop the central limit theorem and associated rates of convergence for a rescaled test statistic based on the bias-corrected distance correlation in high dimensions under some mild regularity conditions and the null hypothesis. Our new theoretical results reveal an interesting phenomenon of blessing of dimensionality for high-dimensional distance correlation inference in the sense that the accuracy of normal approximation can increase with dimensionality. This talk is based on a joint work with Lan Gao, Yingying Fan and Jinchi Lv.

时间：2021年05月10日 10：00

地点：紫金港校区行政楼1417报告厅

联系人：苏中根（zgsu2010@zju.edu.cn）

欢迎大家参加！