The presented models outperform some other well-known models.
📌 3 Key Takeaways
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Objective & Hypothesis:The induction of one or more parameter(s) in parent distributions opened new doors for flexible modeling in modern distribution theory.
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Key Findings & Data:Among well-established generalized (G) classes for flexible modeling, the exponentiated-G, Marshall-Olkin-G and odd log-logistic-G families offer induction of one additional parameter while the beta-G and Kumaraswamy-G classes offer two extra shape parameters.
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Conclusion & Impact:The presented models outperform some other well-known models.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:generalized family · Marshall-Olkin method · maximum likelihood
Abstract
The induction of one or more parameter(s) in parent distributions opened new doors for flexible modeling in modern distribution theory. Among well-established generalized (G) classes for flexible modeling, the exponentiated-G, Marshall-Olkin-G and odd log-logistic-G families offer induction of one additional parameter while the beta-G and Kumaraswamy-G classes offer two extra shape parameters. The Marshall-Olkin-odd-loglogistic-G (MOOLL-G) family serves as an alternative to the beta-G and Kumaraswamy-G classes. A new motivation for the MOOLL-G family for competing risk scenarios, some useful properties, and parameter estimation are addressed. The new log-MOOLL-Weibull regression is useful for analysis of real life data. The accuracy of the estimates and the residuals is addressed via Monte Carlo simulations. The presented models outperform some other well-known models.
CORDEIRO, G. M., TAHIR, M. H., VASCONCELOS, J. C., ORTEGA, E. M., HUSSAIN, M. A. (2022). A new extended log-Weibull regression: Simulations and applications. Hacettepe Journal of Mathematics and Statistics, Cilt: 50 - Sayı: 3, 855–871. https://ajindex.com/articles/529529
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