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A New Test on High-Dimensional Mean Vector Without Any Assumption on Population Covariance Matrix

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  • Shota Katayama
  • Yutaka Kano

Abstract

In this paper, a new test for the equality of the mean vectors between a two groups with the same number of the observations in high-dimensional data. The existing tests for this problem require a strong condition on the population covariance matrix. The proposed test in this paper does not require such conditions for it. This test will be obtained in a general model, that is, the data need not be normally distributed.

Suggested Citation

  • Shota Katayama & Yutaka Kano, 2014. "A New Test on High-Dimensional Mean Vector Without Any Assumption on Population Covariance Matrix," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(24), pages 5290-5304, December.
    • Handle: RePEc:taf:lstaxx:v:43:y:2014:i:24:p:5290-5304
    DOI: 10.1080/03610926.2012.717663
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    Cited by:

    1. Zhou, Bu & Guo, Jia, 2017. "A note on the unbiased estimator of Σ2," Statistics & Probability Letters, Elsevier, vol. 129(C), pages 141-146.
    2. M. Rauf Ahmad, 2019. "A unified approach to testing mean vectors with large dimensions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(4), pages 593-618, December.
    3. Zhang, Jin-Ting & Zhou, Bu & Guo, Jia, 2022. "Linear hypothesis testing in high-dimensional heteroscedastic one-way MANOVA: A normal reference L2-norm based test," Journal of Multivariate Analysis, Elsevier, vol. 187(C).

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