Reliability assessment of heterogeneous Dagum-distributed multi-component stress-strength systems under adaptive hybrid progressive censoring
<p>This paper develops comprehensive inference procedures for multi-component stress-strength reliability models with heterogeneous Dagum-distributed component strengths under adaptive hybrid progressive censoring (AHPC). We propose both classical and Bayesian estimation strategies, including...
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| مؤلفون آخرون: | |
| منشور في: |
2025
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إضافة وسم
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| _version_ | 1864513531552989184 |
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| author | Akram Kohansal (20545673) |
| author2 | Reza Pakyari (18021664) |
| author2_role | author |
| author_facet | Akram Kohansal (20545673) Reza Pakyari (18021664) |
| author_role | author |
| dc.creator.none.fl_str_mv | Akram Kohansal (20545673) Reza Pakyari (18021664) |
| dc.date.none.fl_str_mv | 2025-12-13T18:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.cam.2025.117238 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Reliability_assessment_of_heterogeneous_Dagum-distributed_multi-component_stress-strength_systems_under_adaptive_hybrid_progressive_censoring/30962606 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Manufacturing engineering Mathematical sciences Statistics Multi-component reliability Dagum distribution Bayesian estimation Adaptive hybrid progressive censoring |
| dc.title.none.fl_str_mv | Reliability assessment of heterogeneous Dagum-distributed multi-component stress-strength systems under adaptive hybrid progressive censoring |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>This paper develops comprehensive inference procedures for multi-component stress-strength reliability models with heterogeneous Dagum-distributed component strengths under adaptive hybrid progressive censoring (AHPC). We propose both classical and Bayesian estimation strategies, including maximum likelihood estimators (MLEs), asymptotic confidence intervals, approximate Bayesian estimators, and highest posterior density (HPD) credible intervals. A Markov chain Monte Carlo (MCMC) framework combined with Gibbs sampling is employed to handle complex posterior distributions efficiently. The methodology is validated through simulation studies and real-world data analysis, demonstrating the model’s flexibility in capturing heavy-tailed lifetime behavior and providing actionable insights for reliability planning, component selection, and preventive maintenance scheduling. The results highlight that incorporating AHPC schemes leads to improved estimation accuracy and practical adaptability in reliability engineering.</p><h2>Other Information</h2> <p> Published in: Journal of Computational and Applied Mathematics<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.cam.2025.117238" target="_blank">https://dx.doi.org/10.1016/j.cam.2025.117238</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_6c1dd9a95b7a40991d3a4fe8ebc5b4a7 |
| identifier_str_mv | 10.1016/j.cam.2025.117238 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30962606 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Reliability assessment of heterogeneous Dagum-distributed multi-component stress-strength systems under adaptive hybrid progressive censoringAkram Kohansal (20545673)Reza Pakyari (18021664)EngineeringManufacturing engineeringMathematical sciencesStatisticsMulti-component reliabilityDagum distributionBayesian estimationAdaptive hybrid progressive censoring<p>This paper develops comprehensive inference procedures for multi-component stress-strength reliability models with heterogeneous Dagum-distributed component strengths under adaptive hybrid progressive censoring (AHPC). We propose both classical and Bayesian estimation strategies, including maximum likelihood estimators (MLEs), asymptotic confidence intervals, approximate Bayesian estimators, and highest posterior density (HPD) credible intervals. A Markov chain Monte Carlo (MCMC) framework combined with Gibbs sampling is employed to handle complex posterior distributions efficiently. The methodology is validated through simulation studies and real-world data analysis, demonstrating the model’s flexibility in capturing heavy-tailed lifetime behavior and providing actionable insights for reliability planning, component selection, and preventive maintenance scheduling. The results highlight that incorporating AHPC schemes leads to improved estimation accuracy and practical adaptability in reliability engineering.</p><h2>Other Information</h2> <p> Published in: Journal of Computational and Applied Mathematics<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.cam.2025.117238" target="_blank">https://dx.doi.org/10.1016/j.cam.2025.117238</a></p>2025-12-13T18:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.cam.2025.117238https://figshare.com/articles/journal_contribution/Reliability_assessment_of_heterogeneous_Dagum-distributed_multi-component_stress-strength_systems_under_adaptive_hybrid_progressive_censoring/30962606CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/309626062025-12-13T18:00:00Z |
| spellingShingle | Reliability assessment of heterogeneous Dagum-distributed multi-component stress-strength systems under adaptive hybrid progressive censoring Akram Kohansal (20545673) Engineering Manufacturing engineering Mathematical sciences Statistics Multi-component reliability Dagum distribution Bayesian estimation Adaptive hybrid progressive censoring |
| status_str | publishedVersion |
| title | Reliability assessment of heterogeneous Dagum-distributed multi-component stress-strength systems under adaptive hybrid progressive censoring |
| title_full | Reliability assessment of heterogeneous Dagum-distributed multi-component stress-strength systems under adaptive hybrid progressive censoring |
| title_fullStr | Reliability assessment of heterogeneous Dagum-distributed multi-component stress-strength systems under adaptive hybrid progressive censoring |
| title_full_unstemmed | Reliability assessment of heterogeneous Dagum-distributed multi-component stress-strength systems under adaptive hybrid progressive censoring |
| title_short | Reliability assessment of heterogeneous Dagum-distributed multi-component stress-strength systems under adaptive hybrid progressive censoring |
| title_sort | Reliability assessment of heterogeneous Dagum-distributed multi-component stress-strength systems under adaptive hybrid progressive censoring |
| topic | Engineering Manufacturing engineering Mathematical sciences Statistics Multi-component reliability Dagum distribution Bayesian estimation Adaptive hybrid progressive censoring |