块wigner型矩阵线性谱统计量的中心极限定理

Pub Date : 2021-10-23 DOI:10.1142/s2010326323500065
Zheng-G Wang, Jianfeng Yao
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引用次数: 2

摘要

在随机块模型的激励下,研究了一类具有一定块结构的wigner型矩阵,利用局域律的大偏差界和累积展开公式建立了相应线性谱统计量的CLT。我们将结果应用于随机块模型。具体来说,一类重规格化邻接矩阵将是块wigner型矩阵。进一步,我们表明,对于这种重归一化邻接矩阵的某些估计量,它将不再是wigner型,而是在条目之间具有长期非衰减弱相关性,这种估计量的线性谱统计量仍然与块wigner型矩阵的线性谱统计量具有相同的极限行为,从而能够对随机块模型进行假设检验。
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Central Limit Theorem for Linear Spectral Statistics of Block-Wigner-type Matrices
Motivated by the stochastic block model, we investigate a class of Wigner-type matrices with certain block structures, and establish a CLT for the corresponding linear spectral statistics via the large-deviation bounds from local law and the cumulant expansion formula. We apply the results to the stochastic block model. Specifically, a class of renormalized adjacency matrices will be block-Wigner-type matrices. Further, we show that for certain estimator of such renormalized adjacency matrices, which will be no longer Wigner-type but share long-range non-decaying weak correlations among the entries, the linear spectral statistics of such estimators will still share the same limiting behavior as those of the block-Wigner-type matrices, thus enabling hypothesis testing about stochastic block model.
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