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Minimum distance discrimination rules and success rates for elliptical normal mixtures

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  • Koutras, Markos

Abstract

The linear discriminant function which is optimal for discriminating between normal alternatives is shown to be optimum for the class of elliptical normal mixtures. Some methods for evaluating the probabilities of correct classification of the two-group discrimination problem are discussed.

Suggested Citation

  • Koutras, Markos, 1992. "Minimum distance discrimination rules and success rates for elliptical normal mixtures," Statistics & Probability Letters, Elsevier, vol. 13(4), pages 259-268, March.
    • Handle: RePEc:eee:stapro:v:13:y:1992:i:4:p:259-268
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    Cited by:

    1. Alexander Katzur & Udo Kamps, 2020. "Classification using sequential order statistics," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(1), pages 201-230, March.
    2. A. Batsidis & K. Zografos, 2006. "Discrimination of Observations into One of Two Elliptic Populations based on Monotone Training Samples," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 64(2), pages 221-241, October.
    3. Katzur, Alexander & Kamps, Udo, 2016. "Classification into Kullback–Leibler balls in exponential families," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 75-90.

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