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arXiv 1901.09404math.PR
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方差分布随机矩阵的线性eigenvalue统计量

Linear eigenvalue statistics of random matrices with a variance profile

Kartick Adhikari, Indrajit Jana, Koushik Saha

摘要:

我们给出了一个上界之间的线性特征值统计量,适当的缩放和中心,随机矩阵的方差分布和标准的高斯随机变量的全变差的距离。二阶Poincaré不等式型结果被用来建立界。利用这个界,我们证明了具有不同方差分布的随机矩阵线性特征值统计量的中心极限定理。通过选择适当的方差分布,我们重新建立了一些已知随机矩阵系综线性特征值统计量涨落的已有结果。

英文摘要:

We give an upper bound on the total variation distance between the linear eigenvalue statistic, properly scaled and centred, of a random matrix with a variance profile and the standard Gaussian random variable. The second order Poincaré inequality type result is used to establish the bound. Using this bound we prove Central limit theorem for linear eigenvalue statistics of random matrices with different kind of variance profiles. We re-establish some existing results on fluctuations of linear eigenvalue statistics of some well known random matrix ensembles by choosing appropriate variance profiles.

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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