Universality for the largest eigenvalue of sample covariance matrices with general population

Abstract

This paper is aimed at deriving the universality of the largest eigenvalue of a class of high-dimensional real or complex sample covariance matrices of the form WN=1/2XX* 1/2. Here, X=(xij)M,N is an M× N random matrix with independent entries xij,1≤ i≤ M,1≤ j≤ N such that Exij=0, E|xij|2=1/N. On dimensionality, we assume that M=M(N) and N/M→ d∈(0,∞) as N→∞. For a class of general deterministic positive-definite M× M matrices , under some additional assumptions on the distribution of xij's, we show that the limiting behavior of the largest eigenvalue of WN is universal, via pursuing a Green function comparison strategy raised in [Probab. Theory Related Fields 154 (2012) 341-407, Adv. Math. 229 (2012) 1435-1515] by Erdos, Yau and Yin for Wigner matrices and extended by Pillai and Yin [Ann. Appl. Probab. 24 (2014) 935-1001] to sample covariance matrices in the null case (=I). Consequently, in the standard complex case (Exij2=0), combing this universality property and the results known for Gaussian matrices obtained by El Karoui in [Ann. Probab. 35 (2007) 663-714] (nonsingular case) and Onatski in [Ann. Appl. Probab. 18 (2008) 470-490] (singular case), we show that after an appropriate normalization the largest eigenvalue of WN converges weakly to the type 2 Tracy-Widom distribution TW2. Moreover, in the real case, we show that when is spiked with a fixed number of subcritical spikes, the type 1 Tracy-Widom limit TW1 holds for the normalized largest eigenvalue of WN, which extends a result of F\'eral and P\'ech\'e in [J. Math. Phys. 50 (2009) 073302] to the scenario of nondiagonal and more generally distributed X.

0

Turn this paper into a lesson

ArcXiv compiles a structured reading guide from this paper's metadata: plain-English importance, contributions, prerequisite concepts, which sections to read first, flashcards, and a quiz. Grounded in the abstract, never invented.

Discussion (0)

Sign in to join the discussion.

Loading comments…