A Mixture of Inverse Weibull and Inverse Burr Distributions: Properties, Estimation, and Fitting

Abstract

The new mixture model of the two components of the inverse Weibull and inverse Burr distributions (MIWIBD) is proposed. First, the properties of the investigated mixture model are introduced and the behaviors of the probability density functions and hazard rate functions are displayed. Then, the estimates of the five-dimensional vector of parameters by using the classical method such as the maximum likelihood estimation (MLEs) and the approximation method by using Lindley's approximation are obtained. Finally, a real data set for the proposed mixture model is applied to illustrate the proposed mixture model.

Mixture of Lindley and Lognormal Distributions: Properties, Estimation, and Application

“…Finite mixture models play an important role in modeling such heterogeneous data. Applications of mixture models are especially in clustering and classification, see, for example, Everitt and Hand [1], McLachlan and Peel [2], McLachlan and Basford [3], Titterington et al [4], Lindsay [5], McLachlan and Krishnan [6], Al-Moisheer et al [7,8], and Al-Moisheer [9,10]. In this paper, we will introduce a finite mixture of Lindley and lognormal distributions (MLLND).…”