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Convergence of the empirical spectral distribution function of Beta matrices

By Zhidong Bai, Jiang Hu, Guangming Pan and Wang Zhou


Let $\mathbf{B}_n=\mathbf {S}_n(\mathbf {S}_n+\alpha_n\mathbf {T}_N)^{-1}$, where $\mathbf {S}_n$ and $\mathbf {T}_N$ are two independent sample covariance matrices with dimension $p$ and sample sizes $n$ and $N$, respectively. This is the so-called Beta matrix. In this paper, we focus on the limiting spectral distribution function and the central limit theorem of linear spectral statistics of $\mathbf {B}_n$. Especially, we do not require $\mathbf {S}_n$ or $\mathbf {T}_N$ to be invertible. Namely, we can deal with the case where $p>\max\{n,N\}$ and $p<n+N$. Therefore, our results cover many important applications which cannot be simply deduced from the corresponding results for multivariate $F$ matrices.Comment: Published at in the Bernoulli ( by the International Statistical Institute/Bernoulli Society (

Topics: Mathematics - Probability
Year: 2015
DOI identifier: 10.3150/14-BEJ613
OAI identifier:

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