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Updating a nonlinear discriminant function estimated from a mixture of two inverse Weibull distributions

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  • K. Sultan
  • A. Al-Moisheer

Abstract

In this paper, we investigate the problem of updating a discriminant function on the basis of data of unknown origin. We consider the updating procedure for the nonlinear discriminant function on the basis of two inverse Weibull distributions in situations when the additional observations are mixed or classified. Then, we introduce the nonlinear discriminant function of the underlying model. Also, we calculate the total probabilities of misclassification. In addition, we investigate the performance of the updating procedures through series of simulation experiments by means of the relative efficiencies. Finally, we analyze a simulated data set by using the findings of the paper. Copyright Springer-Verlag 2013

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  • K. Sultan & A. Al-Moisheer, 2013. "Updating a nonlinear discriminant function estimated from a mixture of two inverse Weibull distributions," Statistical Papers, Springer, vol. 54(1), pages 163-175, February.
    • Handle: RePEc:spr:stpapr:v:54:y:2013:i:1:p:163-175
    DOI: 10.1007/s00362-011-0416-z
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    References listed on IDEAS

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    1. Jose Ramon G. Albert & Laurence A. Baxter, 1995. "Applications of the Em Algorithm to the Analysis of Life Length Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 44(3), pages 323-341, September.
    2. Sultan, K.S. & Ismail, M.A. & Al-Moisheer, A.S., 2007. "Mixture of two inverse Weibull distributions: Properties and estimation," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5377-5387, July.
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    Cited by:

    1. Yuebao Wang & Hui Xu & Dongya Cheng & Changjun Yu, 2018. "The local asymptotic estimation for the supremum of a random walk with generalized strong subexponential summands," Statistical Papers, Springer, vol. 59(1), pages 99-126, March.

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