On Structure Testing for Component Covariance Matrices of a High Dimensional Mixture

DOI10.1111/rssb.12248OpenAlexW2616550694MaRDI QIDQ4607210

Wei-Ming Li, Jian-feng Yao

Publication date: 13 March 2018

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1705.04784

zbMATH Keywords

large covariance matrixsphericity testMarčenko-Pastur law

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Analysis of variance and covariance (ANOVA) (62J10)

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CLT for linear spectral statistics of high-dimensional sample covariance matrices in elliptical distributions ⋮ Nonparametric estimate of spectral density functions of sample covariance matrices generated by VARMA models ⋮ Testing high-dimensional covariance matrices under the elliptical distribution and beyond ⋮ Some strong convergence theorems for eigenvalues of general sample covariance matrices ⋮ Tracy-Widom limit for the largest eigenvalue of high-dimensional covariance matrices in elliptical distributions ⋮ Block-diagonal test for high-dimensional covariance matrices ⋮ A bootstrap method for spectral statistics in high-dimensional elliptical models ⋮ On singular values of data matrices with general independent columns ⋮ High-dimensional covariance matrices in elliptical distributions with application to spherical test ⋮ Testing for sphericity in a fixed effects panel data model with time-varying variances

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