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Bootstrap and permutation tests in ANOVA for directional data

Author

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  • Adelaide Figueiredo

    (University of Porto
    LIAAD-INESC TEC)

Abstract

The problem of testing the null hypothesis of a common direction across several populations defined on the hypersphere arises frequently when we deal with directional data. We may consider the Analysis of Variance (ANOVA) for testing such hypotheses. However, for the Watson distribution, a commonly used distribution for modeling axial data, the ANOVA test is only valid for large concentrations. So we suggest to use alternative tests, such as bootstrap and permutation tests in ANOVA. Then, we investigate the performance of these tests for data from Watson populations defined on the hypersphere.

Suggested Citation

  • Adelaide Figueiredo, 2017. "Bootstrap and permutation tests in ANOVA for directional data," Computational Statistics, Springer, vol. 32(4), pages 1213-1240, December.
    • Handle: RePEc:spr:compst:v:32:y:2017:i:4:d:10.1007_s00180-017-0739-x
    DOI: 10.1007/s00180-017-0739-x
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    References listed on IDEAS

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    1. Amaral, G.J.A. & Dryden, I.L. & Wood, Andrew T.A., 2007. "Pivotal Bootstrap Methods for k-Sample Problems in Directional Statistics and Shape Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 695-707, June.
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