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Nonparametric tests for the general multivariate multi-sample problem

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  • Caiya Zhang
  • Zhengyan Lin
  • Jianjun Wu

Abstract

Some nonparametric tests for the multivariate multi-sample problem are proposed in this paper. For the location–scale model, the univariate Kruskal–Wallis test and the bivariate Mardia test are generalised to the multivariate case. For the general multivariate multi-sample problem, a new test based on the Liu-Singh statistic is proposed and the asymptotic null distribution of this test statistic is established under some regularity conditions. The results of simulation show that these tests are more effective than the parametric tests when the assumption of multivariate normal distribution is violated, especially under the scale model or the location–scale model.

Suggested Citation

  • Caiya Zhang & Zhengyan Lin & Jianjun Wu, 2009. "Nonparametric tests for the general multivariate multi-sample problem," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(7), pages 877-888.
    • Handle: RePEc:taf:gnstxx:v:21:y:2009:i:7:p:877-888
    DOI: 10.1080/10485250903111684
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    References listed on IDEAS

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    1. Neuhaus, Georg & Zhu, Li-Xing, 1999. "Permutation Tests for Multivariate Location Problems," Journal of Multivariate Analysis, Elsevier, vol. 69(2), pages 167-192, May.
    2. Rousson, Valentin, 2002. "On Distribution-Free Tests for the Multivariate Two-Sample Location-Scale Model," Journal of Multivariate Analysis, Elsevier, vol. 80(1), pages 43-57, January.
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