journal article·2023-12-01Open access

On the identifiability and statistical features of a new distributional approach with reliability applications

Badr Alnssyan, Zubair Ahmad, Jean-Claude Malela-Majika, Jin-Taek Seong, Wasswa Shafik

AIP Advances· Volume 13· Issue 12

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, heavy-tailed 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.
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Research context

OpenAlex topics: Statistical Distribution Estimation and Applications · Probabilistic and Robust Engineering Design · Reliability and Maintenance Optimization

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Unit of Scientific Research, Applied College, Qassim University 1 , Buraydah 51452, Saudi Arabia

Department of Statistics, Quaid-i-Azam University 2 , Islamabad 44000, Pakistan

Department of Statistics, Faculty of Natural and Agricultural Sciences, University of Pretoria 3 , Hatfield, 0028 Pretoria, South Africa

Graduate School of Data Science, Chonnam National University 4 , Gwangju 61186, Republic of Korea

Dig Connectivity Research Laboratory (DCRLab), Ndejje University 5 , P.O. Box 600040, Kampala, Uganda

Funding

Qassim University

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