Reliability Analysis of Improved Type-II Adaptive Progressively Inverse XLindley Censored Data
This study offers a newly improved Type-II adaptive progressive censoring with data sampled from an inverse XLindley (IXL) distribution for more efficient and adaptive reliability assessments. Through this sampling mechanism, we evaluate the parameters of the IXL distribution, as well as its reliabi...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2075-1680/14/6/437 |
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| author | Refah Alotaibi Mazen Nassar Ahmed Elshahhat |
| author_facet | Refah Alotaibi Mazen Nassar Ahmed Elshahhat |
| author_sort | Refah Alotaibi |
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| description | This study offers a newly improved Type-II adaptive progressive censoring with data sampled from an inverse XLindley (IXL) distribution for more efficient and adaptive reliability assessments. Through this sampling mechanism, we evaluate the parameters of the IXL distribution, as well as its reliability and hazard rate features. In the context of reliability, to handle flexible and time-constrained testing frameworks in high-reliability environments, we formulate maximum likelihood estimators versus Bayesian estimates derived via Markov chain Monte Carlo techniques under gamma priors, which effectively capture prior knowledge. Two patterns of asymptotic interval estimates are constructed through the normal approximation of the classical estimates and of the log-transformed classical estimates. On the other hand, from the Markovian chains, two patterns of credible interval estimates are also constructed. A robust simulation study is carried out to compare the classical and Bayesian point estimation methods, along with the four interval estimation methods. This study’s practical usefulness is demonstrated by its analysis of a real-world dataset. The results reveal that both conventional and Bayesian inferential methods function accurately, with the Bayesian outcomes surpassing those of the conventional method. |
| format | Article |
| id | doaj-art-eb8c56a82cb640d6b29fc724e5316102 |
| institution | Kabale University |
| issn | 2075-1680 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj-art-eb8c56a82cb640d6b29fc724e53161022025-08-20T03:32:27ZengMDPI AGAxioms2075-16802025-06-0114643710.3390/axioms14060437Reliability Analysis of Improved Type-II Adaptive Progressively Inverse XLindley Censored DataRefah Alotaibi0Mazen Nassar1Ahmed Elshahhat2Department of Mathematical Sciences, College of Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi ArabiaFaculty of Technology and Development, Zagazig University, Zagazig 44519, EgyptThis study offers a newly improved Type-II adaptive progressive censoring with data sampled from an inverse XLindley (IXL) distribution for more efficient and adaptive reliability assessments. Through this sampling mechanism, we evaluate the parameters of the IXL distribution, as well as its reliability and hazard rate features. In the context of reliability, to handle flexible and time-constrained testing frameworks in high-reliability environments, we formulate maximum likelihood estimators versus Bayesian estimates derived via Markov chain Monte Carlo techniques under gamma priors, which effectively capture prior knowledge. Two patterns of asymptotic interval estimates are constructed through the normal approximation of the classical estimates and of the log-transformed classical estimates. On the other hand, from the Markovian chains, two patterns of credible interval estimates are also constructed. A robust simulation study is carried out to compare the classical and Bayesian point estimation methods, along with the four interval estimation methods. This study’s practical usefulness is demonstrated by its analysis of a real-world dataset. The results reveal that both conventional and Bayesian inferential methods function accurately, with the Bayesian outcomes surpassing those of the conventional method.https://www.mdpi.com/2075-1680/14/6/437inverse XLindleyreliabilitylikelihoodposterior with Markovian chainshazard ratecensoring |
| spellingShingle | Refah Alotaibi Mazen Nassar Ahmed Elshahhat Reliability Analysis of Improved Type-II Adaptive Progressively Inverse XLindley Censored Data Axioms inverse XLindley reliability likelihood posterior with Markovian chains hazard rate censoring |
| title | Reliability Analysis of Improved Type-II Adaptive Progressively Inverse XLindley Censored Data |
| title_full | Reliability Analysis of Improved Type-II Adaptive Progressively Inverse XLindley Censored Data |
| title_fullStr | Reliability Analysis of Improved Type-II Adaptive Progressively Inverse XLindley Censored Data |
| title_full_unstemmed | Reliability Analysis of Improved Type-II Adaptive Progressively Inverse XLindley Censored Data |
| title_short | Reliability Analysis of Improved Type-II Adaptive Progressively Inverse XLindley Censored Data |
| title_sort | reliability analysis of improved type ii adaptive progressively inverse xlindley censored data |
| topic | inverse XLindley reliability likelihood posterior with Markovian chains hazard rate censoring |
| url | https://www.mdpi.com/2075-1680/14/6/437 |
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