Hadeel AlQadi1, Fatimah M. Alghamdi2, Hamada H. Hassan3, Mohamed E. Mead4, Ahmed Z. Afify5,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2029-2061, 2025, DOI:10.32604/cmes.2025.069801
- 31 August 2025
Abstract Parametric survival models are essential for analyzing time-to-event data in fields such as engineering and biomedicine. While the log-logistic distribution is popular for its simplicity and closed-form expressions, it often lacks the flexibility needed to capture complex hazard patterns. In this article, we propose a novel extension of the classical log-logistic distribution, termed the new exponential log-logistic (NExLL) distribution, designed to provide enhanced flexibility in modeling time-to-event data with complex failure behaviors. The NExLL model incorporates a new exponential generator to expand the shape adaptability of the baseline log-logistic distribution, allowing it to capture a… More >
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