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Central Limit Theorem for Linear Eigenvalue Statistics of the Wigner and Sample Covariance Random Matrices

机译:Wigner和样本协方差随机矩阵的线性特征值统计的中心极限定理

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We consider two classical ensembles of the random matrix theory: the Wigner matrices and sample covariance matrices, and prove Central Limit Theorem for linear eigenvalue statistics under rather weak (comparing with results known before) conditions on the number of derivatives of the test functions and also on the number of the entries moments. Moreover, we develop a universal method which allows one to obtain automatically the bounds for the variance of differentiable test functions, if there is a bound for the variance of the trace of the resolvent of random matrix. The method is applicable not only to the Wigner and sample covariance matrices, but to any ensemble of hermitian or real symmetric random matrices.
机译:我们考虑了随机矩阵理论的两个经典合奏:Wigner矩阵和样本协方差矩阵,并证明了在极弱(与以前已知的结果相比)的条件下,关于测试函数的导数的线性特征值统计的中心极限定理,以及关于输入时刻的数量。此外,我们开发了一种通用方法,如果存在随机矩阵分辨力轨迹的方差,则该方法可以自动获得可区分测试函数方差的边界。该方法不仅适用于Wigner和样本协方差矩阵,而且适用于Hermitian或实对称随机矩阵的任何集合。

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