A Study on The Mixture of Exponentiated-Weibull Distribution PART I (The Method of Maximum Likelihood Estimation) (Published)
Mixtures of measures or distributions occur frequently in the theory and applications of probability and statistics. In the simplest case it may, for example, be reasonable to assume that one is dealing with the mixture in given proportions of a finite number of normal populations with different means or variances. The mixture parameter may also be denumerable infinite, as in the theory of sums of a random number of random variables, or continuous, as in the compound Poisson distribution. The use of finite mixture distributions, to control for unobserved heterogeneity, has become increasingly popular among those estimating dynamic discrete choice models. One of the barriers to using mixture models is that parameters that could previously be estimated in stages must now be estimated jointly: using mixture distributions destroys any additive reparability of the log likelihood function. In this thesis, the maximum likelihood estimators have been obtained for the parameters of the mixture of exponentiated Weibull distribution when sample is available from censoring scheme.The maximum likelihood estimators of the parameters and the asymptotic variance covariance matrix have been obtained. A numerical illustration for these new results is given.
Keywords: Exponentiated Weibull Distributiom (EW), Maximum Likelihood Estimation, Mixture Distribution, Mixture of two Exponentiated Weibull Distribution(MTEW), Moment Estimation, Monte-Carlo Simulation
Binomial Mixture of Erlang Distribution (Published)
In this paper we introduce new Binomial mixture distribution, Binomial-Erlang Distribution( B-Er ) based on the transformation where on the interval [ 0 , ∞ ) by using Moments methods and Laplace transform in mixing binomial distribution with Erlang distribution.
Keywords: Binomial Distribution, Erlang Distribution, Laplace transform., Mixture Distribution