More powerful tests for sparse high-dimensional covariances matrices

DOI10.1016/j.jmva.2016.03.008zbMath1341.62149OpenAlexW2332855008MaRDI QIDQ290714

Liuhua Peng, Wen Zhou, Song Xi Chen

Publication date: 3 June 2016

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2016.03.008

zbMATH Keywords

hypothesis testinghigh-dimensional inferencebanding estimationsparse covariance matrix

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Hypothesis testing in multivariate analysis (62H15)

Related Items (7)

Testing for covariance matrices in time-varying coefficient panel data models with fixed effects ⋮ Step-Down diagnostic analysis for monitoring the covariance matrix of bivariate normal processes ⋮ Homogeneity test of several high-dimensional covariance matrices for stationary processes under non-normality ⋮ Projection tests for high-dimensional spiked covariance matrices ⋮ A review of 20 years of naive tests of significance for high-dimensional mean vectors and covariance matrices ⋮ High-dimensional sphericity test by extended likelihood ratio ⋮ Global one-sample tests for high-dimensional covariance matrices

Uses Software

• Bioconductor
• emdbook

Cites Work

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