On the identifiability and statistical features of a new distributional approach with reliability applications
| dc.contributor.author | Alnssyan, Badr | |
| dc.contributor.author | Ahmad, Zubair | |
| dc.contributor.author | Malela-Majika, Jean-Claude | |
| dc.contributor.author | Seong, Jin-Taek | |
| dc.contributor.author | Shafik, Wasswa | |
| dc.contributor.email | malela.mjc@up.ac.za | en_US |
| dc.date.accessioned | 2024-03-05T10:56:11Z | |
| dc.date.available | 2024-03-05T10:56:11Z | |
| dc.date.issued | 2023-12-08 | |
| dc.description | DATA AVAILABILITY : The datasets that support the findings of this study are provided within the article. | en_US |
| dc.description.abstract | Probability distributions have prominent applications in different sectors. Among these sectors, probability models are mostly used to analyze datasets in engineering. Among the existing probability distributions, the two-parameter Weibull model plays an important role in providing the best fit for engineering and other related datasets. This paper introduces a new method called a novel updated-W (denoted by “NU-W”) family of distributions that is used to develop a new updated form of the Weibull distribution. The proposed updated extension of the Weibull model is referred to as a novel updated Weibull (denoted as NU-Weibull) distribution. Distributional properties such as identifiability, heavytailed characteristic, and rth moment of the NU-W family are derived. The residual life analysis of the NU-Weibull distribution is provided. Finally, two physical applications from civil engineering and reliability sectors are analyzed to demonstrate the application and effectiveness of the NU-Weibull distribution. The data fitting results show that the NU-Weibull distribution is a more suitable and best fit for engineering datasets. | en_US |
| dc.description.department | Statistics | en_US |
| dc.description.librarian | am2024 | en_US |
| dc.description.sdg | None | en_US |
| dc.description.sponsorship | The Deanship of Scientific Research, Qassim University. | en_US |
| dc.description.uri | http://aipadvances.aip.org | en_US |
| dc.identifier.citation | Alnssyan, B., Ahmad, Z., Malela-Majika, J.C. et al. 2023, 'On the identifiability and statistical features of a new distributional approach with reliability applications', AIP Advances, vol. 13, no. 12, art. 125211, pp. 1-15, doi : 10.1063/5.0178555. | en_US |
| dc.identifier.issn | 2158-3226 | |
| dc.identifier.other | 10.1063/5.0178555 | |
| dc.identifier.uri | http://hdl.handle.net/2263/95079 | |
| dc.language.iso | en | en_US |
| dc.publisher | American Institute of Physics | en_US |
| dc.rights | © 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license. | en_US |
| dc.subject | Probability distributions | en_US |
| dc.subject | Sectors | en_US |
| dc.subject | Engineering | en_US |
| dc.subject | Weibull model | en_US |
| dc.title | On the identifiability and statistical features of a new distributional approach with reliability applications | en_US |
| dc.type | Article | en_US |