Comparison between two types of large sample covariance matrices

DOI10.1214/12-AIHP506zbMath1295.15023MaRDI QIDQ2451115

Guangming Pan

Publication date: 26 May 2014

Published in: Annales de l'Institut Henri Poincaré. Probabilités et Statistiques (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aihp/1395856144

zbMATH Keywords

Stieltjes transformstrong convergenceeigenvalueseigenvectorscentral limit theoremssample covariance matrixeigenvalue statistics

Mathematics Subject Classification ID

Central limit and other weak theorems (60F05) Random matrices (probabilistic aspects) (60B20) Random matrices (algebraic aspects) (15B52) Functional limit theorems; invariance principles (60F17) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (11)

CLT for linear spectral statistics of large dimensional sample covariance matrices with dependent data ⋮ Spectral analysis of the Moore-Penrose inverse of a large dimensional sample covariance matrix ⋮ Testing proportionality of two large-dimensional covariance matrices ⋮ Gaussian fluctuations for linear spectral statistics of large random covariance matrices ⋮ A CLT for the LSS of large-dimensional sample covariance matrices with diverging spikes ⋮ Most powerful test against a sequence of high dimensional local alternatives ⋮ High dimensional mean-variance optimization through factor analysis ⋮ Some sphericity tests for high dimensional data based on ratio of the traces of sample covariance matrices ⋮ Asymptotic independence of spiked eigenvalues and linear spectral statistics for large sample covariance matrices ⋮ Substitution principle for CLT of linear spectral statistics of high-dimensional sample covariance matrices with applications to hypothesis testing ⋮ CLT for linear spectral statistics of normalized sample covariance matrices with the dimension much larger than the sample size

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