On testing for an identity covariance matrix when the dimensionality equals or exceeds the sample size

DOI10.1016/j.jspi.2011.07.019zbMath1229.62076OpenAlexW1971771565MaRDI QIDQ643409

Thomas J. Fisher

Publication date: 28 October 2011

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2011.07.019

zbMATH Keywords

hypothesis testinghigh-dimensional data analysisidentity matrix

Mathematics Subject Classification ID

Related Items (19)

Sharp minimax tests for large Toeplitz covariance matrices with repeated observations ⋮ A note on tests for high-dimensional covariance matrices ⋮ Testing Independence via Spectral Moments ⋮ Covariance matrix testing in high dimension using random projections ⋮ Testing the order of a population spectral distribution for high-dimensional data ⋮ Testing high dimensional covariance matrices via posterior Bayes factor ⋮ Variance-corrected tests for covariance structures with high-dimensional data ⋮ Testing block‐diagonal covariance structure for high‐dimensional data ⋮ Hypothesis testing for high-dimensional covariance matrices ⋮ Sphericity and identity test for high-dimensional covariance matrix using random matrix theory ⋮ A note on tests of sphericity and cross-sectional dependence for fixed effects panel model ⋮ Testing identity of high-dimensional covariance matrix ⋮ A robust test for sphericity of high-dimensional covariance matrices ⋮ Tests for high-dimensional covariance matrices ⋮ Global one-sample tests for high-dimensional covariance matrices ⋮ Hypothesis testing for the identity of high-dimensional covariance matrices ⋮ Robust modified classical spherical tests in the presence of outliers ⋮ Detecting positive correlations in a multivariate sample ⋮ CLT for linear spectral statistics of normalized sample covariance matrices with the dimension much larger than the sample size

Cites Work

• A test for the equality of covariance matrices when the dimension is large relative to the sample sizes
• A new test for sphericity of the covariance matrix for high dimensional data
• Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
• A high-dimensional test for the equality of the smallest eigenvalues of a covariance matrix
• On some test criteria for covariance matrix
• Testing for complete independence in high dimensions
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