Updating a nonlinear discriminant function estimated from a mixture of two Burr Type III distributions

ABSTRACT

The main contribution of this paper is is updating a nonlinear discriminant function on the basis of data of unknown origin. Specifically a procedure is developed for updating the nonlinear discriminant function on the basis of two Burr Type III distributions (TBIIID) when the additional observations are mixed or classified. First the nonlinear discriminant function of the assumed model is obtained. Then the total probabilities of misclassification are calculated. In addition a Monte carlo simulation runs are used to compute the relative efficiencies in order to investigate the performance of the developed updating procedures. Finally the results obtained in this paper are illustrated through a real and simulated data set.

KEYWORDS:

• Finite models
• discriminant function
• classified sample
• mixed sample
• combined sample
• efficiency
• simulated data and real data

Acknowledgments

The author is very grateful to the referees for their constructive comments which improved an earlier manuscript of this paper.

Disclosure statement

No potential conflict of interest was reported by the author.