Estimation of a nonlinear discriminant function from a mixture of two exponentiated - Weibull distributions
Abstract
In this paper, a procedure for finding maximum likelihood estimates (MLEs) of the parameters of a finite mixture of two exponentiated-Weibull distributions (MEW) is presented, using classified and unclassified observations. Estimation of a nonlinear discriminant function on the basis of a small sample size is considered. Its performance is investigated by a series of simulation experiments. The simulations conducted for estimating a nonlinear discriminant function by the maximum likelihood method, on the basis of unclassified data drawn from a mixture of the underlying populations suggest that the error rate can be reduced by a substantial percentage for widely separated populations. Generally, the performance of the mixture discrimination procedure relative to the completely classified procedure, measured by total probabilities is good.
Copyright © 2016 A. M. Abd-Elrahman, M. A. Mohammed. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article
A. M. Abd-Elrahman, M. A. Mohammed, Estimation of a nonlinear discriminant function from a mixture of two exponentiated - Weibull distributions, J. Math. Comput. Sci. 6 (2016), 907–921.
BibTeX
@article{Abd-Elrahman2016,
author = {Abd-Elrahman, A. M. and Mohammed, M. A.},
title = {Estimation of a nonlinear discriminant function from a mixture of two exponentiated - Weibull distributions},
journal = {J. Math. Comput. Sci.},
volume = {6},
year = {2016},
pages = {907--921},
doi = {}
}