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Tests for multivariate normality—a critical review with emphasis on weighted $$L^2$$ L 2 -statistics

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  • Bruno Ebner

    (Karlsruhe Institute of Technology (KIT))

  • Norbert Henze

    (Karlsruhe Institute of Technology (KIT))

Abstract

This article gives a synopsis on new developments in affine invariant tests for multivariate normality in an i.i.d.-setting, with special emphasis on asymptotic properties of several classes of weighted $$L^2$$ L 2 -statistics. Since weighted $$L^2$$ L 2 -statistics typically have limit normal distributions under fixed alternatives to normality, they open ground for a neighborhood of model validation for normality. The paper also reviews several other invariant tests for this problem, notably the energy test, and it presents the results of a large-scale simulation study. All tests under study are implemented in the accompanying R-package mnt.

Suggested Citation

  • Bruno Ebner & Norbert Henze, 2020. "Tests for multivariate normality—a critical review with emphasis on weighted $$L^2$$ L 2 -statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 845-892, December.
    • Handle: RePEc:spr:testjl:v:29:y:2020:i:4:d:10.1007_s11749-020-00740-0
    DOI: 10.1007/s11749-020-00740-0
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    1. Baringhaus, L. & Henze, N., 1991. "Limit distributions for measures of multivariate skewness and kurtosis based on projections," Journal of Multivariate Analysis, Elsevier, vol. 38(1), pages 51-69, July.
    2. Liang, Jia-Juan & Bentler, Peter M., 1999. "A t-distribution plot to detect non-multinormality," Computational Statistics & Data Analysis, Elsevier, vol. 30(1), pages 31-44, March.
    3. Srivastava, M. S. & Hui, T. K., 1987. "On assessing multivariate normality based on shapiro-wilk W statistic," Statistics & Probability Letters, Elsevier, vol. 5(1), pages 15-18, January.
    4. Takayuki Yamada & Tetsuto Himeno, 2019. "Estimation of multivariate 3rd moment for high-dimensional data and its application for testing multivariate normality," Computational Statistics, Springer, vol. 34(2), pages 911-941, June.
    5. Vassilly Voinov & Natalie Pya & Rashid Makarov & Yevgeniy Voinov, 2016. "New invariant and consistent chi-squared type goodness-of-fit tests for multivariate normality and a related comparative simulation study," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(11), pages 3249-3263, June.
    6. Fang, Kai-Tai & Li, Run-Ze & Liang, Jia-Juan, 1998. "A multivariate version of Ghosh's T3-plot to detect non-multinormality," Computational Statistics & Data Analysis, Elsevier, vol. 28(4), pages 371-386, October.
    7. Henze, Norbert, 1997. "Limit laws for multivariate skewness in the sense of Móri, Rohatgi and Székely," Statistics & Probability Letters, Elsevier, vol. 33(3), pages 299-307, May.
    8. Annaliisa Kankainen & Sara Taskinen & Hannu Oja, 2007. "Tests of multinormality based on location vectors and scatter matrices," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 16(3), pages 357-379, November.
    9. Tenreiro, Carlos, 2009. "On the choice of the smoothing parameter for the BHEP goodness-of-fit test," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1038-1053, February.
    10. Tenreiro, Carlos, 2011. "An affine invariant multiple test procedure for assessing multivariate normality," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1980-1992, May.
    11. 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.
    12. Tomoya Yamada & Megan Romer & Donald Richards, 2015. "Kurtosis tests for multivariate normality with monotone incomplete data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(3), pages 532-557, September.
    13. H. Holgersson, 2006. "A graphical method for assessing multivariate normality," Computational Statistics, Springer, vol. 21(1), pages 141-149, March.
    14. Norbert Henze & María Dolores Jiménez-Gamero, 2019. "A new class of tests for multinormality with i.i.d. and garch data based on the empirical moment generating function," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 499-521, June.
    15. Klar, Bernhard, 2002. "A Treatment of Multivariate Skewness, Kurtosis, and Related Statistics," Journal of Multivariate Analysis, Elsevier, vol. 83(1), pages 141-165, October.
    16. Henze, Norbert & Jiménez–Gamero, M. Dolores & Meintanis, Simos G., 2019. "Characterizations Of Multinormality And Corresponding Tests Of Fit, Including For Garch Models," Econometric Theory, Cambridge University Press, vol. 35(3), pages 510-546, June.
    17. L. Baringhaus & B. Ebner & N. Henze, 2017. "The limit distribution of weighted $$L^2$$ L 2 -goodness-of-fit statistics under fixed alternatives, with applications," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(5), pages 969-995, October.
    18. Alessandro Manzotti & Adolfo Quiroz, 2001. "Spherical harmonics in quadratic forms for testing multivariate normality," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 10(1), pages 87-104, June.
    19. Piotr Majerski & Zbigniew Szkutnik, 2010. "Approximations to most powerful invariant tests for multinormality against some irregular alternatives," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(1), pages 113-130, May.
    20. Jönsson, Kristian, 2011. "A robust test for multivariate normality," Economics Letters, Elsevier, vol. 113(2), pages 199-201.
    21. Måns Thulin, 2014. "Tests for multivariate normality based on canonical correlations," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 189-208, June.
    22. Kim, Namhyun, 2016. "A robustified Jarque–Bera test for multivariate normality," Economics Letters, Elsevier, vol. 140(C), pages 48-52.
    23. Holger Dette & Axel Munk, 2003. "Some Methodological Aspects of Validation of Models in Nonparametric Regression," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(2), pages 207-244, May.
    24. Jurgen A. Doornik & Henrik Hansen, 2008. "An Omnibus Test for Univariate and Multivariate Normality," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 927-939, December.
    25. L. Baringhaus & N. Henze, 1988. "A consistent test for multivariate normality based on the empirical characteristic function," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 35(1), pages 339-348, December.
    26. Norbert Henze & Jaco Visagie, 2020. "Testing for normality in any dimension based on a partial differential equation involving the moment generating function," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(5), pages 1109-1136, October.
    27. Naito, Kanta, 1998. "Approximation of the Power of Kurtosis Test for Multinormality," Journal of Multivariate Analysis, Elsevier, vol. 65(2), pages 166-180, May.
    28. Tan, Ming & Fang, Hong-Bin & Tian, Guo-Liang & Wei, Gang, 2005. "Testing multivariate normality in incomplete data of small sample size," Journal of Multivariate Analysis, Elsevier, vol. 93(1), pages 164-179, March.
    29. Norbert Henze, 2002. "Invariant tests for multivariate normality: a critical review," Statistical Papers, Springer, vol. 43(4), pages 467-506, October.
    30. Ebner, Bruno, 2012. "Asymptotic theory for the test for multivariate normality by Cox and Small," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 368-379.
    31. Tomasz Górecki & Lajos Horváth & Piotr Kokoszka, 2020. "Tests of Normality of Functional Data," International Statistical Review, International Statistical Institute, vol. 88(3), pages 677-697, December.
    32. Henze, Norbert, 1997. "Extreme smoothing and testing for multivariate normality," Statistics & Probability Letters, Elsevier, vol. 35(3), pages 203-213, October.
    33. Lee, Sangyeol & Ng, Chi Tim, 2011. "Normality test for multivariate conditional heteroskedastic dynamic regression models," Economics Letters, Elsevier, vol. 111(1), pages 75-77, April.
    34. Ming Zhou & Yongzhao Shao, 2014. "A powerful test for multivariate normality," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(2), pages 351-363, February.
    35. Liang, Jiajuan & Pan, William S.Y. & Yang, Zhen-Hai, 2004. "Characterization-based Q-Q plots for testing multinormality," Statistics & Probability Letters, Elsevier, vol. 70(3), pages 183-190, December.
    36. Szekely, Gábor J. & Rizzo, Maria L., 2005. "A new test for multivariate normality," Journal of Multivariate Analysis, Elsevier, vol. 93(1), pages 58-80, March.
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