Spectral analysis of large dimensional Chatterjee's rank correlation matrix

This paper studies the spectral behavior of large dimensional Chatterjee's rank correlation matrix when observations are independent draws from a high-dimensional random vector with independent continuous components.

Limits for the empirical spectral distributions of its two symmetrized versions are established in the proportional high-dimensional regime, one of them being the semicircle law, thereby giving a first example of a correlation matrix with a non-Marchenko--Pastur spectral limit, in contrast to the Pearson, Kendall, and Spearman cases.

We further establish central limit theorems for linear spectral statistics of the symmetrized matrices.

As an important application of this theory, we develop Chatterjee's rank correlation-based tests for the complete independence among the components.