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arXiv 1208.5953math.PR
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Beta矩阵经验谱分布函数的收敛性

Convergence of the empirical spectral distribution function of Beta matrices

Zhidong Bai, Jiang Hu, Guangming Pan, Wang Zhou

摘要:

设$\mathbf{B}_n=\mathbf {S}_n(\mathbf {S}_n+α_n\mathbf {T}_N)^{-1}$,其中$\mathbf {S}_n$和$\mathbf {T}_N$是两个独立的样本协方差矩阵,维度为$p$,样本量分别为$n$和$N$。这就是所谓的Beta矩阵。本文着重研究了$\mathbf {B}_n$的极限谱分布函数和线性谱统计的中心极限定理。特别地,我们不要求$\mathbf {S}_n$或$\mathbf {T}_N$可逆。也就是说,我们可以处理$p>\max\{n,N\}$和$p<n+N$。因此,我们的结果涵盖了许多重要的应用程序,不能简单地推导出相应的多元$F$矩阵的结果。

英文摘要:

Let $\mathbf{B}_n=\mathbf {S}_n(\mathbf {S}_n+α_n\mathbf {T}_N)^{-1}$, 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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