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

K. Sultan () and A. Al-Moisheer

Statistical Papers, 2013, vol. 54, issue 1, 163-175

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

Keywords: Finite mixtures; Sample discriminant function; Classified and unclassified observations; Relative efficiency and Monte Carlo simulations (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00362-011-0416-z

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