A simultaneous testing of the mean vector and the covariance matrix among two populations for high-dimensional data

DOI10.1007/s11749-017-0567-xzbMath1417.62148OpenAlexW2766100642MaRDI QIDQ2414881

Takahiro Nishiyama, Masashi Hyodo

Publication date: 17 May 2019

Published in: Test (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11749-017-0567-x

zbMATH Keywords

asymptotic distributionmultivariate analysishigh-dimensional data analysissimultaneous test

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Hypothesis testing in multivariate analysis (62H15) Asymptotic properties of parametric tests (62F05)

Related Items

A high-dimensional test on linear hypothesis of means under a low-dimensional factor model ⋮ Simultaneous testing of the mean vector and covariance matrix among k populations for high-dimensional data ⋮ Power-Enhanced Simultaneous Test of High-Dimensional Mean Vectors and Covariance Matrices with Application to Gene-Set Testing ⋮ Ratio F test for testing simultaneous hypotheses in models with blocked compound symmetric covariance structure

Cites Work

• Unnamed Item
• Unnamed Item
• Two sample tests for high-dimensional covariance matrices
• Estimations for some functions of covariance matrix in high dimension under non-normality and its applications
• Tests for covariance matrices in high dimension with less sample size
• A two-sample test for high-dimensional data with applications to gene-set testing
• Simultaneous testing of mean vector and covariance matrix for high-dimensional data
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
• Multivariate Statistics