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Central limit theorem for mesoscopic eigenvalue statistics of deformed Wigner matrices and sample covariance matrices

机译:中央极限定理,用于变形的Wigner矩阵和样本协方差矩阵的介观特征统计

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摘要

We consider N by N deformed Wigner random matrices of the form X-N = H-N + A(N), where H-N is a real symmetric or complex Hermitian Wigner matrix and A(N) is a deterministic real bounded diagonal matrix. We prove a universal Central Limit Theorem for the linear eigenvalue statistics of X-N for all mesoscopic scales both in the spectral bulk and at regular edges where the global eigenvalue density vanishes as a square root. The method relies on studying the characteristic function of the linear statistics (Landon and Sosoe (2018)) by using the cumulant expansion method, along with local laws for the Green function of X-N (Ann. Probab. 48 (2020) 963-1001; Probab. Theory Related Fields 169 (2017) 257-352; J. Math. Phys. 54 (2013) 103504) and analytic subordination properties of the free additive convolution (Dallaporta and Fevrier (2019); Random Matrices Theory Appl. 9 (2020) 2050011). We also prove the analogous results for high-dimensional sample covariance matrices.
机译:我们认为n由X-N = H-N + A(n)的形式的变形的Wigner矩阵,其中H-N是真实对称或复杂的隐士Wigner矩阵,并且A(n)是确定性真实界限对角线矩阵。我们证明了X-N的线性特征值统计的通用中央极限定理,用于所有介面尺度和常规边缘的常见边缘密度作为平方根消失。该方法依赖于使用累积扩展方法研究线性统计(Landon和Sosoe(2018))的特征功能,以及XN绿色功能的当地法律(ANN。48(2020)963-1001; Probab。理论相关领域169(2017)257-352; J.数学。物理。54(2013)103504)免费添加剂卷积的分析从属性能(Dallaporta和Fevrier(2019);随机矩阵理论应用。9(2020 )2050011)。我们还证明了高维样本协方差矩阵的类似结果。

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