On the small sample properties of variants of Mardia’s and Srivastava’s kurtosis-based tests for multivariate normality

Zofia Hanusz; Joanna Tarasińska; Zbigniew Osypiuk

Biometrical Letters (2012)

  • Volume: 49, Issue: 2, page 159-175
  • ISSN: 1896-3811

Abstract

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The kurtosis-based tests of Mardia and Srivastava for assessing multivariate normality (MVN) are considered. The asymptotic standard normal distribution of their test statistics, under normality, is often misused for too small samples. The purpose of this paper is to suggest mean-and-variance corrected versions of the Mardia and Srivastava test statistics. Simulation studies evaluating both the true sizes and the powers of original and corrected tests against selected alternatives are presented and compared to the size and the power of the Henze-Zirkler test. The proposed corrected statistics have empirical sizes closer to a nominal significance level than the original ones. It is also shown that the corrected versions of the tests can be more powerful than the original ones.

How to cite

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Zofia Hanusz, Joanna Tarasińska, and Zbigniew Osypiuk. "On the small sample properties of variants of Mardia’s and Srivastava’s kurtosis-based tests for multivariate normality." Biometrical Letters 49.2 (2012): 159-175. <http://eudml.org/doc/268824>.

@article{ZofiaHanusz2012,
abstract = {The kurtosis-based tests of Mardia and Srivastava for assessing multivariate normality (MVN) are considered. The asymptotic standard normal distribution of their test statistics, under normality, is often misused for too small samples. The purpose of this paper is to suggest mean-and-variance corrected versions of the Mardia and Srivastava test statistics. Simulation studies evaluating both the true sizes and the powers of original and corrected tests against selected alternatives are presented and compared to the size and the power of the Henze-Zirkler test. The proposed corrected statistics have empirical sizes closer to a nominal significance level than the original ones. It is also shown that the corrected versions of the tests can be more powerful than the original ones.},
author = {Zofia Hanusz, Joanna Tarasińska, Zbigniew Osypiuk},
journal = {Biometrical Letters},
keywords = {Henze-Zirkler test; true size studies; power studies; heavy-tailed distributions; light-tailed distributions; multivariate normality; graphical methods; test for multivariate normality; power; size},
language = {eng},
number = {2},
pages = {159-175},
title = {On the small sample properties of variants of Mardia’s and Srivastava’s kurtosis-based tests for multivariate normality},
url = {http://eudml.org/doc/268824},
volume = {49},
year = {2012},
}

TY - JOUR
AU - Zofia Hanusz
AU - Joanna Tarasińska
AU - Zbigniew Osypiuk
TI - On the small sample properties of variants of Mardia’s and Srivastava’s kurtosis-based tests for multivariate normality
JO - Biometrical Letters
PY - 2012
VL - 49
IS - 2
SP - 159
EP - 175
AB - The kurtosis-based tests of Mardia and Srivastava for assessing multivariate normality (MVN) are considered. The asymptotic standard normal distribution of their test statistics, under normality, is often misused for too small samples. The purpose of this paper is to suggest mean-and-variance corrected versions of the Mardia and Srivastava test statistics. Simulation studies evaluating both the true sizes and the powers of original and corrected tests against selected alternatives are presented and compared to the size and the power of the Henze-Zirkler test. The proposed corrected statistics have empirical sizes closer to a nominal significance level than the original ones. It is also shown that the corrected versions of the tests can be more powerful than the original ones.
LA - eng
KW - Henze-Zirkler test; true size studies; power studies; heavy-tailed distributions; light-tailed distributions; multivariate normality; graphical methods; test for multivariate normality; power; size
UR - http://eudml.org/doc/268824
ER -

References

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  2. Henze N. (1994): On Mardia’s kurtosis test for multivariate normality. Communication in Statistics - Theory and Methods 23: 1031-1945. Zbl0825.62136
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  10. Mardia K.V., Kanazawa M. (1983): The null distribution of multivariate kurtosis. Communication in Statistics - Simulation and Computation 12: 569-576. Zbl0521.62043
  11. Mardia K.V., Kent J.T., Bibby J.M. (1979): Multivariate Analysis. New York: Academic Press. Zbl0432.62029
  12. Mecklin C.J., Mundfrom D.J. (2004): An appraisal and bibliography of tests for multivariate normality. International Statistical Review 72: 123-138. Zbl1211.62095
  13. R Development Core Team (2008): R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org. 
  14. SAS Institute Inc. (1989): SAS/IML Software: Usage and Reference, Version 6 (First Edition), SAS Institute, Cary, NC. 
  15. Srivastava M.S. (1984). A measure of skewness and kurtosis and a graphical method for assessing multivariate normality. Statistics & Probability Letters 2: 263-267.[Crossref] 
  16. Tiku M.L., Tan W.Y., Balakrishnan N. (1986): Robust Inference. New York: Marcel Dekker. Zbl0597.62017

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