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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=Σ1/2XXΣ1/2\mathcal{W}_N=\Sigma^{1/2}XX^*\Sigma ^{1/2}. Here, X=(xij)M,NX=(x_{ij})_{M,N} is an M×NM\times N random matrix with independent entries xij,1iM,1jNx_{ij},1\leq i\leq M,1\leq j\leq N such that Exij=0\mathbb{E}x_{ij}=0, Exij2=1/N\mathbb{E}|x_{ij}|^2=1/N. On dimensionality, we assume that M=M(N)M=M(N) and N/Md(0,)N/M\rightarrow d\in(0,\infty) as NN\rightarrow\infty. For a class of general deterministic positive-definite M×MM\times M matrices Σ\Sigma, under some additional assumptions on the distribution of xijx_{ij}'s, we show that the limiting behavior of the largest eigenvalue of WN\mathcal{W}_N 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 Erd\H{o}s, 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 (Σ=I\Sigma=I). Consequently, in the standard complex case (Exij2=0\mathbb{E}x_{ij}^2=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\mathcal{W}_N converges weakly to the type 2 Tracy-Widom distribution TW2\mathrm{TW}_2. Moreover, in the real case, we show that when Σ\Sigma is spiked with a fixed number of subcritical spikes, the type 1 Tracy-Widom limit TW1\mathrm{TW}_1 holds for the normalized largest eigenvalue of WN\mathcal {W}_N, which extends a result of F\'{e}ral and P\'{e}ch\'{e} in [J. Math. Phys. 50 (2009) 073302] to the scenario of nondiagonal Σ\Sigma and more generally distributed XX.

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