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.

翻译：本文研究限制高维肯德尔级相关矩阵的光谱分布。 允许基础人口具有一般依赖性结构。 其结果不再遵循通用的Mar\ u{ c}enko- Pastur 法律, 这部法律是全新的。 这是与依赖性相关的级关系矩阵的第一个结果。 作为应用, 我们研究肯德尔的级相关矩阵, 用于使用普通共变矩阵进行多变量正常分布。 通过这些结果, 我们进一步了解肯德尔级相关矩阵及其与样本变量/关系矩阵的联系。