JOURNAL ARTICLE

Statistical Inference Using Partially Accelerated Life Test Model for the Weibull Population Mean Unified Hybrid Censored Data.

  • Published In: International Journal of Reliability, Quality & Safety Engineering, 2024, v. 31, n. 3. P. 1 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Rabie, Abdalla; Abd-Elrahman, Ayman M. 3 of 3

Abstract

In this paper, estimates of the parameters and the acceleration factor of a new distribution known as the Weibull Population Mean Distribution (WPMD) are explored. We propose the use of the model suggested by Abd-Elrahman [A better alternative to the generalized Bilal distribution: A new model and applications, Int. J. Reliab. Qual. Saf. Eng. 30(6) (2023) 2350027, doi: 10.1142/S0218539323500274 ] for estimating these parameters. To accomplish this, we employ the maximum likelihood estimation method via a Constant-Stress Partially Accelerated Life Test (CSPALT) model. In this study, we have utilized Unified Hybrid (UH) censored data obtained from the WPMD. Confidence intervals for the model parameters and the acceleration factor are constructed based on the approximate Fisher information matrix. The average and Mean Squared Error (MSE) of estimates are computed to evaluate the performance of the proposed method under the CSPALT model. Additionally, a real data set is provided for illustrative purposes, and a numerical analysis of the UH Censoring Scheme (UHCS) is carried out. Finally, concluding remarks are presented. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Reliability, Quality & Safety Engineering. 2024/06, Vol. 31, Issue 3, p1
  • Document Type:Article
  • Subject Area:Engineering
  • Publication Date:2024
  • ISSN:0218-5393
  • DOI:10.1142/S0218539324500141
  • Accession Number:177838791
  • Copyright Statement:Copyright of International Journal of Reliability, Quality & Safety Engineering is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.