McDonald Generalized Power Weibull Distribution: Properties, and Applications

Authors

Shei B. Sayibu, Department of Statistics, Faculty of Physical Sciences, University for Development Studies, Box 1350, West Africa, Tamale, GhanaFollow
A. Luguterah, Department of Statistics, Faculty of Physical Sciences, University for Development Studies, Box 1350, West Africa, Tamale, GhanaFollow
A. Luguterah, Department of Statistics and Actuarial Science, School of Mathematical Sciences, C. K. Tedam University of Technology and Applied Sciences, Box 24, West Africa, Navrongo, GhanaFollow
S. Nasiru, Department of Statistics and Actuarial Science, School of Mathematical Sciences, C. K. Tedam University of Technology and Applied Sciences, Box 24, West Africa, Navrongo, GhanaFollow

Author Country (or Countries)

Ghana

Abstract

This research introduces a novel six-parameter model called the McDonald Generalized Power Weibull distribution. The model contains several sub-models that prove highly valuable in modeling real-life scenarios, including the McDonald Weibull, McDonald exponential, McDonald Nadarajah-Haghighi, beta generalized power Weibull distribution, and Kumaraswamy generalized power distributions, among others. The proposed model demonstrates suitability in modeling survival/reliability data, accommodating various hazard failure rates such as increasing, decreasing, unimodal (upside-down bathtub), modified bathtub, and reversed J-shape. Various properties of the new model are investigated, including moments, quantiles, incomplete moments, moment-generating functions, and order statistics. The maximum likelihood estimation method is employed to estimate the model parameters. The study concludes by illustrating the flexibility of the proposed model through the use of lifetime data to demonstrate its applicability.

Digital Object Identifier (DOI)

https://dx.doi.org/10.18576/jsap/130121

Recommended Citation

B. Sayibu, Shei; Luguterah, A.; Luguterah, A.; and Nasiru, S. (2024) "McDonald Generalized Power Weibull Distribution: Properties, and Applications," Journal of Statistics Applications & Probability: Vol. 13: Iss. 1, Article 21.
DOI: https://dx.doi.org/10.18576/jsap/130121
Available at: https://digitalcommons.aaru.edu.jo/jsap/vol13/iss1/21