Exact Separation of Eigenvalues of Large Dimensional Noncentral Sample Covariance Matrices

08/07/2023

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by   Zhidong Bai, et al.

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Let _n =1/n(_n + ^1/2_n _n)(_n + ^1/2_n _n)^* where _n is a p × n matrix with independent standardized random variables, _n is a p × n non-random matrix, representing the information, and _n is a p × p non-random nonnegative definite Hermitian matrix. Under some conditions on _n _n^* and _n, it has been proved that for any closed interval outside the support of the limit spectral distribution, with probability one there will be no eigenvalues falling in this interval for all p sufficiently large. The purpose of this paper is to carry on with the study of the support of the limit spectral distribution, and we show that there is an exact separation phenomenon: with probability one, the proper number of eigenvalues lie on either side of these intervals.