Test on the linear combinations of covariance matrices in high-dimensional data

DOI10.1007/s00362-019-01110-1zbMath1477.62139OpenAlexW2938670849MaRDI QIDQ2066518

Chen Wang, Jiang Hu, Chao Zhang, Zhi-Dong Bai

Publication date: 14 January 2022

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00362-019-01110-1

zbMATH Keywords

U-statisticCLTcovariance matricesmulti-sample test

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Applications of statistics to actuarial sciences and financial mathematics (62P05) Hypothesis testing in multivariate analysis (62H15)

Related Items

Testing high-dimensional mean vector with applications. A normal reference approach

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Two sample tests for high-dimensional covariance matrices
• On two simple and effective procedures for high dimensional classification of general populations
• Testing linear hypotheses of mean vectors for high-dimension data with unequal covariance matrices
• On testing the equality of high dimensional mean vectors with unequal covariance matrices
• A review of 20 years of naive tests of significance for high-dimensional mean vectors and covariance matrices
• Testing the equality of several covariance matrices with fewer observations than the dimension
• Corrections to LRT on large-dimensional covariance matrix by RMT
• Multivariate small sample tests for two-way designs with applications to industrial statistics
• CLT for linear spectral statistics of large-dimensional sample covariance matrices.
• On divergence tests for composite hypotheses under composite likelihood
• A two-sample test for high-dimensional data with applications to gene-set testing
• Test on the linear combinations of mean vectors in high-dimensional data
• Optimal hypothesis testing for high dimensional covariance matrices
• A study of two high-dimensional likelihood ratio tests under alternative hypotheses
• Empirical properties of asset returns: stylized facts and statistical issues
• Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings
• Multi-sample test for high-dimensional covariance matrices