Central limit theorem for linear eigenvalue statistics for a tensor product version of sample covariance matrices

DOI10.1007/s10959-017-0741-9zbMath1394.15025arXiv1602.08613OpenAlexW2584766696WikidataQ59522764 ScholiaQ59522764MaRDI QIDQ1661592

Y. Aharonov

Publication date: 16 August 2018

Published in: Journal of Theoretical Probability (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1602.08613

zbMATH Keywords

central limit theoremrandom matrixsample covariance matrixlinear eigenvalue statistics

Mathematics Subject Classification ID

Central limit and other weak theorems (60F05) Random matrices (probabilistic aspects) (60B20) Eigenvalues, singular values, and eigenvectors (15A18) Random matrices (algebraic aspects) (15B52)

Related Items (10)

Limiting Spectral Distribution for Large Sample Covariance Matrices with Graph-Dependent Elements ⋮ On the empirical spectral distribution for certain models related to sample covariance matrices with different correlations ⋮ Marchenko–Pastur law with relaxed independence conditions ⋮ Marchenko-Pastur law for a random tensor model ⋮ On Sufficient Conditions in the Marchenko--Pastur Theorem ⋮ On the CLT for Linear Eigenvalue Statistics of a Tensor Model of Sample Covariance Matrices ⋮ Strong limit theorem for largest entry of large-dimensional random tensor ⋮ Limiting behavior of largest entry of random tensor constructed by high-dimensional data ⋮ Quantum stream ciphers: impossibility of unconditionally strong algorithms ⋮ On spectral distribution of sample covariance matrices from large dimensional and large \(k\)-fold tensor products

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