CLT for linear spectral statistics of normalized sample covariance matrices with the dimension much larger than the sample size

DOI10.3150/14-BEJ599zbMath1385.60044arXiv1506.00458MaRDI QIDQ2348737

Binbin Chen, Guangming Pan

Publication date: 15 June 2015

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1506.00458

zbMATH Keywords

central limit theoremlinear spectral statisticspopulation covariance matrix

Mathematics Subject Classification ID

Hypothesis testing in multivariate analysis (62H15) Central limit and other weak theorems (60F05) Random matrices (probabilistic aspects) (60B20)

Related Items (7)

Testing high dimensional covariance matrices via posterior Bayes factor ⋮ Global eigenvalue fluctuations of random biregular bipartite graphs ⋮ Asymptotic normality for eigenvalue statistics of a general sample covariance matrix when \(p/n \to \infty\) and applications ⋮ A CLT for the LSS of large-dimensional sample covariance matrices with diverging spikes ⋮ Asymptotic joint distribution of extreme eigenvalues and trace of large sample covariance matrix in a generalized spiked population model ⋮ Testing identity of high-dimensional covariance matrix ⋮ High-dimensional sphericity test by extended likelihood ratio

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