Ro’ya Al-Dibi’i
Mathematical Department, Irbid National University, Irbid, Jordan
Anas A. Ababneh
Department of Statistics, Yarmouk University, Jordan
Wael Mahmoud Mohammad Salameh
Faculty of Information Technology, Abu Dhabi University, Abu Dhabi, United Arab Emirates
Mayada Abualhomos
Mathematics Department, faculty of Science, Applied Science Private University (ASU), Amman, Jordan
Ahmad A. Abubaker
Faculty of Computer Studies, Arab Open University, Saudi Arabia
Jenan Shtayat
Department of mathematics, Yarmouk University, Irbid 21163, Jordan
Ahmed Atallah Alsaraireh
The university of Jordan–Aqaba Department of computer information systems, Jordan.
This study proposes a novel finite mixture model based on the Exponentiated Generalized Weibull Exponential (EGWE) distribution to effectively model heterogeneous lifetime data. The EGWE distribution is known for its flexibility in capturing various hazard rate shapes, including increasing, decreasing, bathtub, and unimodal forms. By incorporating multiple EGWE components into a finite mixture framework, the proposed model enhances its ability to fit complex, multimodal datasets. Parameter estimation is conducted using Maximum Likelihood Estimation (MLE), and the performance of the estimators is evaluated via Monte Carlo simulations using bias and standard error measures. Applications to real datasets demonstrate that the proposed model provides a superior fit to the data compared to single-component models, as evidenced by lower Akaike Information Criterion (AIC) values.
