TY - JOUR
AU - Wang, Qinwen
AB - In this paper, we investigate the limiting spectral distribution of a high-dimensional Kendall’s rank correlation matrix. The underlying population is allowed to have a general dependence structure. The result no longer follows the generalized Marc̆enko-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.
TI - On eigenvalues of a high-dimensional Kendall’s rank correlation matrix with dependence
JF - Science China Mathematics
DO - 10.1007/s11425-022-2031-2
DA - 2023-11-01
UR - https://www.deepdyve.com/lp/springer-journals/on-eigenvalues-of-a-high-dimensional-kendall-s-rank-correlation-matrix-jY0Uc04pa3
SP - 2615
EP - 2640
VL - 66
IS - 11
DP - DeepDyve
ER -