• APPLICAION OF NON-PARAMETRIC DENSITY ESTIMATION FOR PARAMETERS OF AN INVERSE GAUSSIAN – INVERSE WEIBULL MIXTURE MODEL

Sultan, Ahmed, M. M, Almubty, Reem Z. M.*

Abstract


A technique is introduced for estimating the parameters of a mixture of two distributions. The mixture has a two parameters Inverse Weibull distribution and a two parameters Inverse Gaussian one mixed with a given mixing proportion. The technique is based on a nonparametric density estimator for the mixture using a sample from the given mixture. The minimum distance estimation (MDE) method is used for estimating the parameters of the mixture when the empirical distribution function (EDF) is replaced by the corresponding nonparametric density estimator. A Monte Carlo of size 10000 is used to show the performance of the proposed method. Results from the proposed method show a better performance.


Keywords


Non-parametric density, Inverse Weibull, Inverse Gaussian, Gaussian kernel, hybrid methods, Cramer von Mises statistic.

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