报告题目：Limiting distribution for extreme eigenvalues of large Fisher matrices with divergent numbers of spikes

报 告 人：解俊山，河南大学副教授

照    片：

邀 请 人：冶继民 教授

报告时间：2020年11月19日下午2:30-4：30

报告平台：腾讯会议（ID: 712 635 767）

报告人简介：解俊山，河南大学环球360会员登录副教授，副院长，统计学专业博士生导师。2012年获浙江大学概率论与数理统计专业博士学位，美国明尼苏达大学、香港浸会大学访问学者。主要研究方向为概率极限理论、随机矩阵理论和高维统计推断。主持完成国家自然科学基金两项，主持河南省自然科学基金面上项目、河南省高校重点科研项目和河南省高校基本科研业务基金（优青培育）项目等。以第一作者（或通讯作者）在Journal of Theoretical Probability, Science China: Mathematics等国内外学术期刊发表SCI论文20余篇。

报告摘要：Consider the p×p matrix that is the product of a population covariance matrix and the inverse of another population covariance matrix. Suppose that their difference has a divergent rank with respect to p, when two samples of sizes n and T from the two populations are available, we construct its corresponding sample version. In the regime of high dimension where both n and T are proportional to p, we investigate the limiting laws for extreme (spiked) eigenvalues of the sample (spiked) Fisher matrix when the number of spikes is divergent and these spikes are unbounded.