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A new test for multivariate normality

  • Szekely, Gábor J.
  • Rizzo, Maria L.
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    We propose a new class of rotation invariant and consistent goodness-of-fit tests for multivariate distributions based on Euclidean distance between sample elements. The proposed test applies to any multivariate distribution with finite second moments. In this article we apply the new method for testing multivariate normality when parameters are estimated. The resulting test is affine invariant and consistent against all fixed alternatives. A comparative Monte Carlo study suggests that our test is a powerful competitor to existing tests, and is very sensitive against heavy tailed alternatives.

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    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 93 (2005)
    Issue (Month): 1 (March)
    Pages: 58-80

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    Handle: RePEc:eee:jmvana:v:93:y:2005:i:1:p:58-80
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    1. L. Baringhaus & N. Henze, 1988. "A consistent test for multivariate normality based on the empirical characteristic function," Metrika, Springer, vol. 35(1), pages 339-348, December.
    2. Romeu, J. L. & Ozturk, A., 1993. "A Comparative Study of Goodness-of-Fit Tests for Multivariate Normality," Journal of Multivariate Analysis, Elsevier, vol. 46(2), pages 309-334, August.
    3. Thas, O. & Ottoy, J. P., 2003. "Some generalizations of the Anderson-Darling statistic," Statistics & Probability Letters, Elsevier, vol. 64(3), pages 255-261, September.
    4. Henze, Norbert & Wagner, Thorsten, 1997. "A New Approach to the BHEP Tests for Multivariate Normality," Journal of Multivariate Analysis, Elsevier, vol. 62(1), pages 1-23, July.
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