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Robust divergence-based tests of hypotheses for simple step-stress accelerated life-testing under Gamma lifetime distributions

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dc.contributor.authorBalakrishnan, Narayanaswamy
dc.contributor.authorJaenada Malagón, María
dc.contributor.authorPardo, Leandro
dc.date.accessioned2026-01-26T18:44:08Z
dc.date.available2026-01-26T18:44:08Z
dc.date.issued2026-01-18
dc.descriptionThe registered version of this article, first published in “Journal of Computational and Applied Mathematics 483 (2026), 117362 ", is available online at the publisher's website: Elsevier, https://doi.org/10.1016/j.cam.2026.117362
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en “Journal of Computational and Applied Mathematics 483 (2026), 117362", está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.cam.2026.117362
dc.description.abstractMany modern devices are highly reliable, with long lifetimes before their failure. Conducting reliability tests under actual use conditions may require therefore impractically long experimental times to gather sufficient data for developing accurate inference. To address this, Accelerated Life Tests (ALTs) are often used in industrial experiments to induce product degradation and eventual failure more quickly by increasing certain environmental stress factors. Data collected under such increased stress conditions are analyzed, and results are then extrapolated to normal operating conditions. These tests typically involve a small number of devices and so pose significant challenges, such as interval-censoring. As a result, the outcomes are particularly sensitive to outliers in the data. Additionally, a comprehensive analysis requires more than just point estimation; inferential methods such as confidence intervals and hypothesis testing are essential to fully assess the reliability behaviour of the product. This paper presents robust statistical methods based on minimum divergence estimators for analyzing ALT data of highly reliable devices under step-stress conditions and Gamma lifetime distributions. Robust test statistics generalizing the Rao test and divergence-based tests for testing linear null hypothesis are then developed. These hypotheses include in particular tests for the significance of the identified stress factors and for the validity of the assumption of exponential lifetimes.en
dc.description.provenanceMade available in DSpace on 2026-01-26T18:44:08Z (GMT). No. of bitstreams: 1 Robust divergence-based tests of hypotheses for simple step-stress accelerated life-testing under Gamma lifetime distributions. César Jaenada Malagón.pdf: 4154481 bytes, checksum: 8b359cbe0dcd291d9dcce368bea4a621 (MD5) Previous issue date: 2026-01-18en
dc.description.versionversión publicada
dc.identifier.citationNarayanaswamy Balakrishnan, María Jaenada, Leandro Pardo, (2026). Robust divergence-based tests of hypotheses for simple step-stress accelerated life-testing under Gamma lifetime distributions, Journal of Computational and Applied Mathematics 483, 117362. https://doi.org/10.1016/j.cam.2026.117362
dc.identifier.doihttps://doi.org/10.1016/j.cam.2026.117362
dc.identifier.eissn1879-1778
dc.identifier.issn0377-0427
dc.identifier.urihttps://hdl.handle.net/20.500.14468/31574
dc.journal.titleJournal of Computational and Applied Mathematics
dc.journal.volume483
dc.language.isoen
dc.page.final16
dc.page.initial1
dc.publisherElsevier
dc.relation.centerFacultad de Ciencias
dc.relation.departmentEstadística, Investigación Operativa y Cálculo Numérico
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject1209 Estadística
dc.subject.keywordsAccelerated life-testsen
dc.subject.keywordsDivergence-based inferential methodsen
dc.subject.keywordsReliability analysisen
dc.subject.keywordsRobust tests of hypothesesen
dc.titleRobust divergence-based tests of hypotheses for simple step-stress accelerated life-testing under Gamma lifetime distributionsen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication0dae66b3-1736-4f17-8275-ed3e18524f90
relation.isAuthorOfPublication.latestForDiscovery0dae66b3-1736-4f17-8275-ed3e18524f90
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