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Permutation Tests for Multivariate Location Problems

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  • Neuhaus, Georg
  • Zhu, Li-Xing

Abstract

The paper presents some permutation test procedures for multivariate location. The tests are based on projected univariate versions of multivariate data. For one-sample cases, the tests are affine invariant and strictly distribution-free for the symmetric null distribution with elliptical direction and their permutation counterparts are conditionally distribution-free when the underlying null distribution of the sample is angularly symmetric. For multi-sample cases, the tests are also affine invariant and permutation counterparts of the tests are conditionally distribution-free for any null distribution with certain continuity. Hence all of the tests in this paper are exactly valid. Furthermore, the equivalence, in the large sample sense, between the tests and their permutation counterparts are established. The power behavior of the tests and of their permutation counterparts under local alternative are investigated. A simulation study shows the tests to perform well compared with some existing tests in the literature, particularly when the underlying null distribution is symmetric whether light-tailed or heavy-tailed. For revealing the influence of data sparseness on the effect of the test, some simulations with different dimensions are also performed.

Suggested Citation

  • Neuhaus, Georg & Zhu, Li-Xing, 1999. "Permutation Tests for Multivariate Location Problems," Journal of Multivariate Analysis, Elsevier, vol. 69(2), pages 167-192, May.
    • Handle: RePEc:eee:jmvana:v:69:y:1999:i:2:p:167-192
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    Cited by:

    1. 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.
    2. Stefano Bonnini & Getnet Melak Assegie & Kamila Trzcinska, 2024. "Review about the Permutation Approach in Hypothesis Testing," Mathematics, MDPI, vol. 12(17), pages 1-29, August.

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