On a model selection problem from high-dimensional sample covariance matrices

DOI10.1016/j.jmva.2011.05.005zbMath1219.62090OpenAlexW2071911508MaRDI QIDQ634554

Jiaqi Chen, Bernard Delyon, Jian-feng Yao

Publication date: 16 August 2011

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10722/135512

zbMATH Keywords

cross-validationhigh-dimensional dataMarčenko-Pastur distributionorder selectionlarge sample covariance matrices

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Asymptotic distribution theory in statistics (62E20) Central limit and other weak theorems (60F05) Random matrices (algebraic aspects) (15B52)

Related Items (4)

Numerical implementation of the QuEST function ⋮ Estimation of the population spectral distribution from a large dimensional sample covariance matrix ⋮ Random matrix theory in statistics: a review ⋮ On generalized expectation-based estimation of a population spectral distribution from high-dimensional data

Cites Work

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• Concentration inequalities for the spectral measure of random matrices
• Spectrum estimation for large dimensional covariance matrices using random matrix theory
• Statistical eigen-inference from large Wishart matrices
• On the distribution of the largest eigenvalue in principal components analysis
• Analysis of the limiting spectral distribution of large dimensional random matrices
• ON ESTIMATION OF THE POPULATION SPECTRAL DISTRIBUTION FROM A HIGH‐DIMENSIONAL SAMPLE COVARIANCE MATRIX
• DISTRIBUTION OF EIGENVALUES FOR SOME SETS OF RANDOM MATRICES

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