Heterogeneous data modeling with two-component Weibull-Poisson distribution

• Published in: Journal of applied statistics Vol. 40; no. 11; pp. 2451 - 2461
• Main Authors: Erisoglu, Uelkue, Erisoglu, Murat, Calis, Nazif
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 01-11-2013; Taylor & Francis Ltd
• Subjects: Algorithms; Applied statistics; Computer simulation; Datasets; EM algorithm; Estimates; fatigue; Flexibility; heterogeneous data; Mathematical models; Maximization; Maximum likelihood method; mixed distribution; mixture of WP distributions; oral irrigators; Parameter estimation; Performance evaluation; Probability distribution; Simulation; Studies
• ISSN: 0266-4763; 1360-0532
• DOI: 10.1080/02664763.2013.818108

The mixture distribution models are more useful than pure distributions in modeling of heterogeneous data sets. The aim of this paper is to propose mixture of Weibull-Poisson (WP) distributions to model heterogeneous data sets for the first time. So, a powerful alternative mixture distribution is created for modeling of the heterogeneous data sets. In the study, many features of the proposed mixture of WP distributions are examined. Also, the expectation maximization (EM) algorithm is used to determine the maximum-likelihood estimates of the parameters, and the simulation study is conducted for evaluating the performance of the proposed EM scheme. Applications for two real heterogeneous data sets are given to show the flexibility and potentiality of the new mixture distribution.