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Multivariate skewing mechanisms: A unified perspective based on the transformation approach

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  • Ley, Christophe
  • Paindaveine, Davy

Abstract

In recent years, models for (possibly multivariate) skewed distributions have become more and more popular. In the univariate case, Ferreira and Steel (2006) [Ferreira, J.T.A.S., Steel, M.F.J., 2006. A constructive representation of univariate skewed distributions. J. Amer. Statist. Assoc. 101, 823-829] introduced general skewing mechanisms in order to compare existing skewing methods in a common framework and to ease construction of new such methods according to the needs in given situations. In this paper, we make use of the classical transformation approach to define alternative skewing mechanisms for the same purpose. While keeping all the nice features of Ferreira and Steel's skewing mechanisms (flexibility, surjectivity, the possibility of retaining prespecified characteristics of the original symmetric distribution, etc.), our skewing mechanisms, unlike theirs, can easily be extended to the multivariate case. We describe our skewing schemes, investigate their main properties, and illustrate their effects on standard (multi)normal distributions by means of a few examples. Finally, we briefly discuss their relevance in the context of optimal symmetry testing.

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  • Ley, Christophe & Paindaveine, Davy, 2010. "Multivariate skewing mechanisms: A unified perspective based on the transformation approach," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1685-1694, December.
    • Handle: RePEc:eee:stapro:v:80:y:2010:i:23-24:p:1685-1694
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    References listed on IDEAS

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    3. Ley, Christophe & Paindaveine, Davy, 2010. "On the singularity of multivariate skew-symmetric models," Journal of Multivariate Analysis, Elsevier, vol. 101(6), pages 1434-1444, July.
    4. Christophe Ley & Davy Paindaveine, 2009. "Le Cam optimal tests for symmetry against Ferreira and Steel's general skewed distributions," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(8), pages 943-967.
    5. M. Jones, 2004. "Families of distributions arising from distributions of order statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 13(1), pages 1-43, June.
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    Cited by:

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    2. Christophe Ley, 2014. "Flexible Modelling in Statistics: Past, present and Future," Working Papers ECARES ECARES 2014-42, ULB -- Universite Libre de Bruxelles.
    3. Rubio, Francisco Javier & Steel, Mark F. J., 2014. "Bayesian modelling of skewness and kurtosis with two-piece scale and shape transformations," MPRA Paper 57102, University Library of Munich, Germany.
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    7. Jupp, P.E. & Regoli, G. & Azzalini, A., 2016. "A general setting for symmetric distributions and their relationship to general distributions," Journal of Multivariate Analysis, Elsevier, vol. 148(C), pages 107-119.
    8. M. C. Jones, 2015. "On Families of Distributions with Shape Parameters," International Statistical Review, International Statistical Institute, vol. 83(2), pages 175-192, August.
    9. Gurjeet Dhesi & Bilal Shakeel & Marcel Ausloos, 2021. "Modelling and forecasting the kurtosis and returns distributions of financial markets: irrational fractional Brownian motion model approach," Annals of Operations Research, Springer, vol. 299(1), pages 1397-1410, April.
    10. Jonas Baillien & Irène Gijbels & Anneleen Verhasselt, 2023. "Flexible asymmetric multivariate distributions based on two-piece univariate distributions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(1), pages 159-200, February.

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