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Fluctuations of Matrix Elements of Regular Functions of Gaussian Random Matrices

机译:高斯随机矩阵正则函数矩阵元素的涨落

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We find the limit of the variance and prove the Central Limit Theorem (CLT) for the matrix elements φ jk (M), j,k=1,…,n of a regular function φ of the Gaussian matrix M (GOE and GUE) as its size n tends to infinity. We show that unlike the linear eigenvalue statistics Tr φ(M), a traditional object of random matrix theory, whose variance is bounded as n→∞ and the CLT is valid for Tr φ(M)−E{Tr φ(M)}, the variance of φ jk (M) is O(1), and the CLT is valid for . This shows the role of eigenvectors in the forming of the asymptotic regime of various functions (statistics) of random matrices. Our proof is based on the use of the Fourier transform as a basic characteristic function, unlike the Stieltjes transform and moments, used in majority of works of the field. We also comment on the validity of analogous results for other random matrices. Keywords Random matrices - Linear eigenvalue statistics - Central Limit Theorem
机译:我们找到方差的极限并证明高斯正则函数φ的矩阵元素φ jk (M),j,k = 1,…,n的中心极限定理(CLT)矩阵M(GOE和GUE)的大小n趋于无穷大。我们证明,与线性特征值统计Trφ(M)不同,随机矩阵理论的传统对象的方差为n→∞,并且CLT对Trφ(M)-E {Trφ(M)}有效,φ jk (M)的方差为O(1 / n),而CLT对有效。这表明特征向量在随机矩阵的各种函数(统计量)的渐近状态的形成中的作用。我们的证明是基于傅立叶变换作为基本特征函数的使用,这与本领域大多数作品中使用的Stieltjes变换和矩不同。我们还评论了其他随机矩阵类似结果的有效性。关键词随机矩阵-线性特征值统计-中心极限定理

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