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A distance based two-sample test of means difference for multivariate datasets

Author

Listed:
  • Alexander Novoselsky

    (Weizmann Institute of Science)

  • Eugene Kagan

    (Ariel University)

Abstract

In the paper we present a new test for comparison of the means of multivariate samples with unknown distributions. The test is based on the comparison of the distributions of the distances between the samples’ elements and their means using univariate two-sample Kolmogorov–Smirnov test. The activity of the suggested method is illustrated by numerical analysis of the real-world and simulated data.

Suggested Citation

  • Alexander Novoselsky & Eugene Kagan, 2024. "A distance based two-sample test of means difference for multivariate datasets," Statistical Papers, Springer, vol. 65(8), pages 4861-4874, October.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:8:d:10.1007_s00362-024-01576-8
    DOI: 10.1007/s00362-024-01576-8
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    References listed on IDEAS

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    1. Qiu, Zhiping & Chen, Jianwei & Zhang, Jin-Ting, 2021. "Two-sample tests for multivariate functional data with applications," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    2. Yujun Wu & Marc G. Genton & Leonard A. Stefanski, 2006. "A Multivariate Two-Sample Mean Test for Small Sample Size and Missing Data," Biometrics, The International Biometric Society, vol. 62(3), pages 877-885, September.
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