Bayesian and Non-Bayesian for Generalized Kavya–Manoharan Exponential Distribution Based on Progressive-Stress ALT Under Generalized Progressive Hybrid Censoring Scheme
Accelerated life tests are vital in reliability studies, especially as new technologies create highly reliable products to meet market demand and competition. Progressive stress accelerated life test (PSALT) allows continual stress adjustments. For reliability and survival analysis in accelerated li...
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MDPI AG
2025-05-01
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| author | Ehab M. Almetwally Osama M. Khaled Hisham M. Almongy Haroon M. Barakat |
| author_facet | Ehab M. Almetwally Osama M. Khaled Hisham M. Almongy Haroon M. Barakat |
| author_sort | Ehab M. Almetwally |
| collection | DOAJ |
| description | Accelerated life tests are vital in reliability studies, especially as new technologies create highly reliable products to meet market demand and competition. Progressive stress accelerated life test (PSALT) allows continual stress adjustments. For reliability and survival analysis in accelerated life studies, generalized progressive hybrid censoring (GPHC) is very important. The research on GPHC in PSALT models is lacking despite its growing importance. Binomial elimination and generalized progressive hybrid censoring augment PSALT in this investigation. This research examines PSALT under the Generalized Kavya–Manoharan exponential distribution based on the GPHC scheme. Using gamma prior, maximum likelihood, and Bayesian techniques, estimate model parameters. Squared error and entropy loss functions yield Bayesian estimators using informational priors in simulation and non-informative priors in application. Various censoring schemes are calculated using Monte Carlo simulation. The methodology is demonstrated using two real-world accelerated life test data sets. |
| format | Article |
| id | doaj-art-529a0408eb434ebe8b7a5a9499fe4e04 |
| institution | OA Journals |
| issn | 2075-1680 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj-art-529a0408eb434ebe8b7a5a9499fe4e042025-08-20T02:24:26ZengMDPI AGAxioms2075-16802025-05-0114641010.3390/axioms14060410Bayesian and Non-Bayesian for Generalized Kavya–Manoharan Exponential Distribution Based on Progressive-Stress ALT Under Generalized Progressive Hybrid Censoring SchemeEhab M. Almetwally0Osama M. Khaled1Hisham M. Almongy2Haroon M. Barakat3Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi ArabiaDepartment of Mathematics and Computer Science, Faculty of Science, Port Said University, Port Said 42521, EgyptDepartment of Applied Statistics and Insurance, Faculty of Commerce, Mansoura University, El-Mansoura 35516, EgyptDepartment of Mathematics, Faculty of Science, Zagazig University, Zagazig 44759, EgyptAccelerated life tests are vital in reliability studies, especially as new technologies create highly reliable products to meet market demand and competition. Progressive stress accelerated life test (PSALT) allows continual stress adjustments. For reliability and survival analysis in accelerated life studies, generalized progressive hybrid censoring (GPHC) is very important. The research on GPHC in PSALT models is lacking despite its growing importance. Binomial elimination and generalized progressive hybrid censoring augment PSALT in this investigation. This research examines PSALT under the Generalized Kavya–Manoharan exponential distribution based on the GPHC scheme. Using gamma prior, maximum likelihood, and Bayesian techniques, estimate model parameters. Squared error and entropy loss functions yield Bayesian estimators using informational priors in simulation and non-informative priors in application. Various censoring schemes are calculated using Monte Carlo simulation. The methodology is demonstrated using two real-world accelerated life test data sets.https://www.mdpi.com/2075-1680/14/6/410accelerated life testingcensoring sampleprogressive-stress modelBayesian estimationlinear cumulative exposure model |
| spellingShingle | Ehab M. Almetwally Osama M. Khaled Hisham M. Almongy Haroon M. Barakat Bayesian and Non-Bayesian for Generalized Kavya–Manoharan Exponential Distribution Based on Progressive-Stress ALT Under Generalized Progressive Hybrid Censoring Scheme Axioms accelerated life testing censoring sample progressive-stress model Bayesian estimation linear cumulative exposure model |
| title | Bayesian and Non-Bayesian for Generalized Kavya–Manoharan Exponential Distribution Based on Progressive-Stress ALT Under Generalized Progressive Hybrid Censoring Scheme |
| title_full | Bayesian and Non-Bayesian for Generalized Kavya–Manoharan Exponential Distribution Based on Progressive-Stress ALT Under Generalized Progressive Hybrid Censoring Scheme |
| title_fullStr | Bayesian and Non-Bayesian for Generalized Kavya–Manoharan Exponential Distribution Based on Progressive-Stress ALT Under Generalized Progressive Hybrid Censoring Scheme |
| title_full_unstemmed | Bayesian and Non-Bayesian for Generalized Kavya–Manoharan Exponential Distribution Based on Progressive-Stress ALT Under Generalized Progressive Hybrid Censoring Scheme |
| title_short | Bayesian and Non-Bayesian for Generalized Kavya–Manoharan Exponential Distribution Based on Progressive-Stress ALT Under Generalized Progressive Hybrid Censoring Scheme |
| title_sort | bayesian and non bayesian for generalized kavya manoharan exponential distribution based on progressive stress alt under generalized progressive hybrid censoring scheme |
| topic | accelerated life testing censoring sample progressive-stress model Bayesian estimation linear cumulative exposure model |
| url | https://www.mdpi.com/2075-1680/14/6/410 |
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