报告题目：Limiting Spectral Distribution of Large Dimensional Spearman's Rank Correlation Matrices

报告时间：2022-05-04  09:50 - 10:30

报告人：王成 副教授  上海交通大学

腾讯会议ID：746-395-986

Abstract: In this paper, we study the empirical spectral distribution of Spearman's rank correlation matrices, under the assumption that the observations are independent and identically distributed random vectors and the features are correlated. We show that the limiting spectral distribution is the generalized Marcenko-Pastur law with the covariance matrix of the observation after standardized transformation. With these results, we compare several classical covariance/correlation matrices including the sample covariance matrix, Pearson's correlation matrix, Kendall's correlation matrix and Spearman's correlation matrix.