  This paper investigates limiting spectral distribution of a high-dimensional
Kendall's rank correlation matrix. The underlying population is allowed to have
general dependence structure. The result no longer follows the generalized
Mar\u{c}enko-Pastur law, which is brand new. It's the first result on rank
correlation matrices with dependence. As applications, we study the Kendall's
rank correlation matrix for multivariate normal distributions with a general
covariance matrix. From these results, we further gain insights on Kendall's
rank correlation matrix and its connections with the sample
covariance/correlation matrix.
