The limiting spectral distribution for large sample covariance matrices with unboundedm-dependent entries

DOI10.1080/03610926.2014.963621zbMath1352.60044OpenAlexW2516104353MaRDI QIDQ2832658

Guangyu Yang, Meng Wei, Ling-Ling Yang

Publication date: 11 November 2016

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2014.963621

zbMATH Keywords

Stieltjes transformlimiting spectral distributionsample covariance matrices\(m\)-dependence

Mathematics Subject Classification ID

Multivariate analysis (62H99) Random matrices (probabilistic aspects) (60B20) Strong limit theorems (60F15)

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Cites Work

• On the distribution of the roots of certain symmetric matrices
• Strong convergence of the empirical distribution of eigenvalues of sample covariance matrices with a perturbation matrix
• Spectral analysis of large dimensional random matrices
• Strong convergence of the empirical distribution of eigenvalues of large dimensional random matrices
• Limiting spectral distribution of normalized sample covariance matrices with \(p/n\to 0\)
• A central limit theorem for m-dependent random variables with unbounded m
• Limiting Spectral Distribution for Large Sample Covariance Matrices withm-Dependent Elements
• DISTRIBUTION OF EIGENVALUES FOR SOME SETS OF RANDOM MATRICES
• ON THE DISTRIBUTION OF ROOTS OF CERTAIN DETERMINANTAL EQUATIONS