A CLT for linear spectral statistics of large random information-plus-noise matrices

Marwa Banna, Jamal Najim and Jianfeng Yao

Stochastic Processes and their Applications, 2020, vol. 130, issue 4, 2250-2281

Abstract: Consider a matrix Yn=σnXn+An, where σ>0 and Xn=(xijn) is a N×n random matrix with i.i.d. real or complex standardized entries and An is a N×n deterministic matrix with bounded spectral norm. The fluctuations of the linear spectral statistics of the eigenvalues: Tracef(YnYn∗)=∑i=1Nf(λi),(λi)eigenvalues ofYnYn∗,are shown to be Gaussian, in the case where f is a smooth function of class C3 with bounded support, and in the regime where both dimensions of matrix Yn go to infinity at the same pace. The CLT is expressed in terms of vanishing Lévy–Prokhorov distance between the linear statistics’ distribution and a centered Gaussian probability distribution, the variance of which depends upon N and n and may not converge. The proof combines ideas from [2,18] and [32].

Keywords: Large random matrices; Linear statistics of the eigenvalues; Central limit theorem (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1016/j.spa.2019.06.017

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