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Multi-sample test for high-dimensional covariance matrices

Author

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  • Chao Zhang
  • Zhidong Bai
  • Jiang Hu
  • Chen Wang

Abstract

In this paper, we propose a new test statistic for testing the equality of high-dimensional covariance matrices for multiple populations. The proposed test statistic generalizes the test of the equality of two population covariance matrices proposed by Li and Chen (2012).

Suggested Citation

  • Chao Zhang & Zhidong Bai & Jiang Hu & Chen Wang, 2018. "Multi-sample test for high-dimensional covariance matrices," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(13), pages 3161-3177, July.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:13:p:3161-3177
    DOI: 10.1080/03610926.2017.1350272
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    Cited by:

    1. Chen, Canyi & Xu, Wangli & Zhu, Liping, 2022. "Distributed estimation in heterogeneous reduced rank regression: With application to order determination in sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    2. Peng Sun & Yincai Tang & Mingxiang Cao, 2022. "Homogeneity Test of Multi-Sample Covariance Matrices in High Dimensions," Mathematics, MDPI, vol. 10(22), pages 1-19, November.
    3. Zhidong Bai & Jiang Hu & Chen Wang & Chao Zhang, 2021. "Test on the linear combinations of covariance matrices in high-dimensional data," Statistical Papers, Springer, vol. 62(2), pages 701-719, April.
    4. Harrar, Solomon W. & Kong, Xiaoli, 2022. "Recent developments in high-dimensional inference for multivariate data: Parametric, semiparametric and nonparametric approaches," Journal of Multivariate Analysis, Elsevier, vol. 188(C).

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