ASYMPTOTIC NORMALITY FOR EIGENVALUE STATISTICS OF A GENERAL SAMPLE COVARIANCE MATRIX WHEN P/N →∞ AND APPLICATIONS

报告人：李曾

报告地点：腾讯会议ID:973 650 604

报告时间：2022年12月21日星期三09:30-10:30

报告摘要:

The asymptotic normality for a large family of eigenvalue statistics of a general sample covariance matrix is derived under the ultra-high dimensional setting, that is, when the dimension to sample size ratio p/n→∞. Based on this CLT result, we extend the covariance matrix test problem to the new ultrahigh dimensional context, and apply it to test a matrix-valued white noise. Simulation experiments are conducted for the investigation of finite-sample properties of the general asymptotic normality of eigenvalue statistics, as well as the two developed tests.

主讲人简介：

李曾，南方科技大学统计与数据科学系副教授。2017年获得香港大学统计与精算学系博士学位，2017-2019年先后在美国华盛顿大学、宾夕法尼亚州立大学从事研究助理和博士后研究工作，并于2019年入职南方科技大学。主要研究领域为随机矩阵理论、高维统计分析等，研究成果发表于The Annals of Statistics, Scandinavian Journal of Statistics 等国际统计学期刊。