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A high-dimensional spatial rank test for two-sample location problems

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  • Feng, Long
  • Zhang, Xiaoxu
  • Liu, Binghui

Abstract

In high-dimensional situations, the traditional multivariate sign- or rank-based procedures for the two-sample location testing problems are ineffective, since in the construction of the test statistics, the scatter matrix to be inverted is singular. To solve this problem, many high-dimensional spatial sign or rank tests have been proposed, some of which are very efficient. However, most of these existing tests no longer work in very high dimensional situations, which only allows the dimension of variables to be the square of the sample sizes at most, hence are restrictive for practical applications. On this ground, a new high-dimensional spatial rank test is proposed in this paper, which is invariant under scalar transformations, maintains the efficiency advantage of spatial-rank-based testing methods, and could even allow the dimension to grow almost exponentially with the sample sizes. The theoretical results of the proposed test are established, followed by some convincing numerical results and two real data analyses.

Suggested Citation

  • Feng, Long & Zhang, Xiaoxu & Liu, Binghui, 2020. "A high-dimensional spatial rank test for two-sample location problems," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    • Handle: RePEc:eee:csdana:v:144:y:2020:i:c:s0167947319302440
    DOI: 10.1016/j.csda.2019.106889
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    References listed on IDEAS

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    1. Long Feng & Changliang Zou & Zhaojun Wang, 2016. "Multivariate-Sign-Based High-Dimensional Tests for the Two-Sample Location Problem," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 721-735, April.
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    6. Feng, Long & Sun, Fasheng, 2015. "A note on high-dimensional two-sample test," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 29-36.
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    Cited by:

    1. 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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