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Title: On eigenvalues of a high-dimensional Kendalls rank correlation matrix with dependence Authors:  Zeng Li - Southern University of Science and Technology (China) [presenting]
Cheng Wang - Shanghai Jiao Tong University (China)
Qinwen Wang - Fudan University (China)
Abstract: Limiting spectral distribution of a high dimensional Kendall's rank correlation matrix is investigated. The underlying population is allowed to have a general dependence structure. The result no longer follows the generalized Marchenko-Pastur law, which is brand new. It is the first result on rank correlation matrices with dependence. As applications, we study Kendall's rank correlation matrix for multivariate normal distributions with a general covariance matrix. From these results, we further gain insights into Kendall's rank correlation matrix and its connections with the sample covariance/correlation matrix.