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A study of the effect of kurtosis on discriminant analysis under elliptical populations

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  • Arevalillo, Jorge M.
  • Navarro, Hilario

Abstract

This paper is concerned with the role some parameters indexing four important families within the multivariate elliptically contoured distributions play as indicators of multivariate kurtosis. The problem is addressed for the exponential power family, for a subclass of the Kotz family and for the Pearson type II and type VII distributions. Once such a problem is analyzed, we study the effect these parameters have, as kurtosis indicators, on binary discriminant analysis by exploring their relationship with the error rate of the Bayes discriminant rule. The effect is analyzed under mild conditions on the kernel function generating the elliptical density. Some numerical examples are given in order to illustrate our theoretical insights and findings.

Suggested Citation

  • Arevalillo, Jorge M. & Navarro, Hilario, 2012. "A study of the effect of kurtosis on discriminant analysis under elliptical populations," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 53-63.
  • Handle: RePEc:eee:jmvana:v:107:y:2012:i:c:p:53-63
    DOI: 10.1016/j.jmva.2012.01.011
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    References listed on IDEAS

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    1. Samuel Kotz & Edith Seier, 2009. "An analysis of quantile measures of kurtosis: center and tails," Statistical Papers, Springer, vol. 50(3), pages 553-568, June.
    2. Cambanis, Stamatis & Huang, Steel & Simons, Gordon, 1981. "On the theory of elliptically contoured distributions," Journal of Multivariate Analysis, Elsevier, vol. 11(3), pages 368-385, September.
    3. Oja, Hannu, 1983. "Descriptive statistics for multivariate distributions," Statistics & Probability Letters, Elsevier, vol. 1(6), pages 327-332, October.
    4. Wang, Jin, 2009. "A family of kurtosis orderings for multivariate distributions," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 509-517, March.
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    Cited by:

    1. Arevalillo, Jorge M. & Navarro, Hilario, 2015. "A note on the direction maximizing skewness in multivariate skew-t vectors," Statistics & Probability Letters, Elsevier, vol. 96(C), pages 328-332.

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