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A nonparametric approach to high-dimensional k-sample comparison problems

Author

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  • Subhadeep Mukhopadhyay
  • Kaijun Wang

Abstract

SummaryHigh-dimensional $k$-sample comparison is a common task in applications. We construct a class of easy-to-implement distribution-free tests based on new nonparametric tools and unexplored connections with spectral graph theory. The test is shown to have various desirable properties and a characteristic exploratory flavour that has practical consequences for statistical modelling. Numerical examples show that the proposed method works surprisingly well across a broad range of realistic situations.

Suggested Citation

  • Subhadeep Mukhopadhyay & Kaijun Wang, 2020. "A nonparametric approach to high-dimensional k-sample comparison problems," Biometrika, Biometrika Trust, vol. 107(3), pages 555-572.
    • Handle: RePEc:oup:biomet:v:107:y:2020:i:3:p:555-572.
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    File URL: http://hdl.handle.net/10.1093/biomet/asaa015
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    Cited by:

    1. Tianming Zhu & Jin-Ting Zhang, 2022. "Linear hypothesis testing in high-dimensional one-way MANOVA: a new normal reference approach," Computational Statistics, Springer, vol. 37(1), pages 1-27, March.
    2. Luai Al-Labadi & Forough Fazeli Asl & Zahra Saberi, 2022. "A Bayesian nonparametric multi-sample test in any dimension," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(2), pages 217-242, June.

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