Rainfall data modeling using improved adaptive type-II progressively censored Weibull-exponential samples
Abstract To get enough data from experiments that last for a long time, a recently unique improved adaptive Type-II progressive censoring technique has been suggested. This study, taking this scheme into consideration, concentrates on some conventional and Bayesian estimation tasks for parameter and...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-80529-5 |
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| author | Refah Alotaibi Mazen Nassar Ahmed Elshahhat |
| author_facet | Refah Alotaibi Mazen Nassar Ahmed Elshahhat |
| author_sort | Refah Alotaibi |
| collection | DOAJ |
| description | Abstract To get enough data from experiments that last for a long time, a recently unique improved adaptive Type-II progressive censoring technique has been suggested. This study, taking this scheme into consideration, concentrates on some conventional and Bayesian estimation tasks for parameter and reliability indicators, where the underlying distribution is the Weibull-exponential. From a traditional point of view, the likelihood methodology is explored for gaining point and approximate confidence interval estimates. Apart from the standard method, the Bayesian methodology is investigated to obtain the Bayesian point and credible intervals by taking advantage of the Markov chain Monte Carlo technique and the squared error loss function. To differentiate between the traditional and Bayesian estimates, a simulation analysis proceeds under various conditions. In order to put the suggested strategies into application, a pair of rainfall data sets are evaluated and numerous precision criteria are employed to pick the best progressive censoring plan. |
| format | Article |
| id | doaj-art-60344993f0be4291b2fadb9eabb9816f |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-60344993f0be4291b2fadb9eabb9816f2025-08-20T02:31:41ZengNature PortfolioScientific Reports2045-23222024-12-0114113010.1038/s41598-024-80529-5Rainfall data modeling using improved adaptive type-II progressively censored Weibull-exponential samplesRefah Alotaibi0Mazen Nassar1Ahmed Elshahhat2Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman UniversityDepartment of Statistics, Faculty of Science, King Abdulaziz UniversityFaculty of Technology and Development, Zagazig UniversityAbstract To get enough data from experiments that last for a long time, a recently unique improved adaptive Type-II progressive censoring technique has been suggested. This study, taking this scheme into consideration, concentrates on some conventional and Bayesian estimation tasks for parameter and reliability indicators, where the underlying distribution is the Weibull-exponential. From a traditional point of view, the likelihood methodology is explored for gaining point and approximate confidence interval estimates. Apart from the standard method, the Bayesian methodology is investigated to obtain the Bayesian point and credible intervals by taking advantage of the Markov chain Monte Carlo technique and the squared error loss function. To differentiate between the traditional and Bayesian estimates, a simulation analysis proceeds under various conditions. In order to put the suggested strategies into application, a pair of rainfall data sets are evaluated and numerous precision criteria are employed to pick the best progressive censoring plan.https://doi.org/10.1038/s41598-024-80529-5Weibull-exponentialReliability estimationImproved adaptive progressiveInterval estimationLikelihood estimationBayesian estimation |
| spellingShingle | Refah Alotaibi Mazen Nassar Ahmed Elshahhat Rainfall data modeling using improved adaptive type-II progressively censored Weibull-exponential samples Scientific Reports Weibull-exponential Reliability estimation Improved adaptive progressive Interval estimation Likelihood estimation Bayesian estimation |
| title | Rainfall data modeling using improved adaptive type-II progressively censored Weibull-exponential samples |
| title_full | Rainfall data modeling using improved adaptive type-II progressively censored Weibull-exponential samples |
| title_fullStr | Rainfall data modeling using improved adaptive type-II progressively censored Weibull-exponential samples |
| title_full_unstemmed | Rainfall data modeling using improved adaptive type-II progressively censored Weibull-exponential samples |
| title_short | Rainfall data modeling using improved adaptive type-II progressively censored Weibull-exponential samples |
| title_sort | rainfall data modeling using improved adaptive type ii progressively censored weibull exponential samples |
| topic | Weibull-exponential Reliability estimation Improved adaptive progressive Interval estimation Likelihood estimation Bayesian estimation |
| url | https://doi.org/10.1038/s41598-024-80529-5 |
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