Enhancing Lifetime Data Modeling With the Modified Version of Weibull Distribution: A Comparative Approach
Arjun Kumar Gaire1, Shahid Mohammad2 & Yogendra Bahadur Gurung3 Show affiliations
- Department of Science and Humanities, Khwopa Engineering College, Purbanchal University, Bhaktapur, Nepal
- Department of Mathematics, University of Wisconsin, Oshkosh, United States
- Central Department of Population Studies, Kathmandu, Tribhuvan University, Kirtipur, Nepal
Journal of Mathematics and Statistics, Volume 21, Issue 1, pp. 15–25 · doi.org/10.3844/jmssp.2025.15.25
Abstract
This study introduces a new two-parameter univariate distribution, a modified version of the Weibull distribution, for a non-negative realm. Originating from a framework introduced by Azzalini and Capitanio, the distribution exhibits a monotonically increasing hazard rate. It offers closed-form expressions for key statistical properties, including reliability functions, moments, moment-generating functions, and order statistics. An expression for random number generation is formulated. The maximum likelihood is employed for parameter inference. A simulation study shows the reliability of these estimators under various conditions. To assess the model's practical applicability, it is fitted to two real-world datasets: waiting times and tensile strength of carbon fiber. Results from quantile-quantile plots and validity tools tests show that the proposed model outperforms compared to selected alternatives, which shows its superior flexibility and accuracy to fit real-world problems.
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Open access. This article is licensed under a Creative Commons Attribution 4.0 International Licence (CC BY 4.0), as printed on the article, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source. No permission is needed.
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Gaire, A. K., Mohammad, S. & Gurung, Y. B. (2025). Enhancing Lifetime Data Modeling With the Modified Version of Weibull Distribution: A Comparative Approach. Journal of Mathematics and Statistics, 21(1), 15–25. https://doi.org/10.3844/jmssp.2025.15.25
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