Limiting distribution for extreme eigenvalues of large spiked sample Fisher-matrices

发布时间：2023-04-04 作者：浏览次数：

Speaker:	解俊山教授	DateTime:	2023年4月10日（周一）16：30-17：30
Brief Introduction to Speaker: 河南大学数学与统计学院教授，博士生导师。浙江大学概率论与数理统计专业博士，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等省级项目多项。以第一作者（或通讯作者）在J.Multivariate Anal., J.Theor. Probab.等国内外学术期刊发表SCI论文20余篇。		Place:	6号楼M109研究生讨论室
Abstract:Spiked Fisher-matrix is defined by the product of a population covariance matrix and the inverse of another population covariance matrix, and their difference has a small rank compared to the dimension. When two samples of sizes n and T from the two p-dimensional populations are available, we can get its corresponding sample spiked Fisher-matrix. In the regime of high dimension where both n and T are proportional to p, we investigate the limiting laws for extreme eigenvalues of the spiked sample Fisher-matrix when the number of spikes is divergent and these spikes are unbounded.