The convergence on spectrum of sample covariance matrices for information-plus-noise type data

DOI10.1007/s11766-012-2818-7zbMath1265.15055OpenAlexW2139396647MaRDI QIDQ1931150

Junshan Xie

Publication date: 24 January 2013

Published in: Applied Mathematics. Series B (English Edition) (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11766-012-2818-7

zbMATH Keywords

Stieltjes transformrandom matricessample covariance matrixlimiting spectral distribution

Mathematics Subject Classification ID

Random matrices (probabilistic aspects) (60B20) Strong limit theorems (60F15) Random matrices (algebraic aspects) (15B52)

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Cites Work

• Unnamed Item
• On limit theorem for the eigenvalues of product of two random matrices
• Strong convergence of the empirical distribution of eigenvalues of sample covariance matrices with a perturbation matrix
• Analysis of the limiting spectral distribution of large dimensional information-plus-noise type matrices
• Central limit theorem for linear eigenvalue statistics of random matrices with independent entries
• Necessary and sufficient condition that the limit of Stieltjes transforms is a Stieltjes transform
• On the empirical distribution of eigenvalues of a class of large dimensional random matrices
• Strong convergence of the empirical distribution of eigenvalues of large dimensional random matrices
• On the empirical distribution of eigenvalues of large dimensional information-plus-noise-type matrices
• DISTRIBUTION OF EIGENVALUES FOR SOME SETS OF RANDOM MATRICES

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