报告题目：Asymptotic normality for eigenvalue statistic of a general sample covariance matrix when p/n → ∞ and application
报 告 人：李曾 副教授 南方科技大学
报告时间：2021年12月16日 9：00-10：00
报告地点：腾讯会议：578-770-059
会议链接：https://meeting.tencent.com/dm/quwRfcnbYjFD
校内联系人：韩月才 hanyc@jlu.edu.cn
报告摘要：The asymptotic normality for a large family of eigenvalue statistics of ageneral 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 first extend the covariance matrix test problem to the new ultra-high dimensional context. Then as a second application, we develop a new test for the separable covariance structure of 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.
报告人简介：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。
