🤖 AI Summary
This study addresses the challenge posed by lifetime data that often exhibit skewness and heavy- or light-tailed characteristics, for which existing distributional models lack both flexibility and analytical tractability. To bridge this gap, the authors propose a novel two-parameter Shiha distribution and systematically derive its key statistical properties, including the moment-generating function, quantile function, entropy, and stress-strength reliability—providing, for the first time, a closed-form expression for the latter. Through comprehensive Monte Carlo simulations and empirical analyses of multiple real-world lifetime datasets, the Shiha distribution demonstrates superior fitting accuracy and model adaptability compared to established distributions, highlighting its practical utility and modeling advantages in reliability engineering and environmental science.
📝 Abstract
This paper introduces a new two-parameter distribution, referred to as the Shiha distribution, which provides a flexible model for skewed lifetime data with either heavy or light tails. The proposed distribution is applicable to various fields, including reliability engineering, environmental studies, and related areas. We derive its main statistical properties, including the moment generating function, moments, hazard rate function, quantile function, and entropy. The stress--strength reliability parameter is also derived in closed form. A simulation study is conducted to evaluate its performance. Applications to several real data sets demonstrate that the Shiha distribution consistently provides a superior fit compared with established competing models, confirming its practical effectiveness for lifetime data analysis.