EDGE UNIVERSALITY OF CORRELATION MATRICES

Let ${\tilde X_{M \times N}}$ be a rectangular data matrix with independent real-valued entries $[{\tilde x_{ij}}]$ satisfying .. ${\tilde x_{ij}}$ and .. $\tilde x_{ij}^2$ = $\frac{1}{M}$ , N, M . .. These entries have a subexponential decay at the tails. We will be working in the regime N/M = d N , lim N.. d N . 0,1, .. In this paper we prove the edge universality of correlation matrices X . X, where the rectangular matrix X (called the standardized matrix) is obtained by normalizing each column of the data matrix $\tilde X$ by its Euclidean norm. Our main result states that asymptotically the Appoint (k . 1) correlation functions of the extreme eigenvalues (at both edges of the spectrum) of the correlation matrix X . X converge to those of the Gaussian correlation matrix, that is, Tracy-Widom law, and, thus, in particular, the largest and the smallest eigenvalues of X . X after appropriate centering and rescaling converge to the Tracy-Widom distribution. The asymptotic distribution of extreme eigenvalues of the Gaussian correlation matrix has been worked out only recently. As a corollary of the main result in this paper, we also obtain that the extreme eigenvalues of Gaussian correlation matrices are asymptotically distributed according to the Tracy-Widom law. The proof is based on the comparison of Green functions, but the key obstacle to be surmounted is the strong dependence of the entries of the correlation matrix. We achieve this via a novel argument which involves comparing the moments of product of the entries of the standardized data matrix to those of the raw data matrix. Our proof strategy may be extended for proving the edge universality of other random matrix ensembles with dependent entries and hence is of independent interest.