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Article: Statistical inference and prediction for the Weibull process with incomplete observations

TitleStatistical inference and prediction for the Weibull process with incomplete observations
Authors
KeywordsAMSAA model
Confidence intervals
Goodness-of-fit test
Nonhomogeneous Poisson process
Prediction limits
Reliability growth
Weibull process
Issue Date2008
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda
Citation
Computational Statistics And Data Analysis, 2008, v. 52 n. 3, p. 1587-1603 How to Cite?
AbstractIn this article, statistical inference and prediction analyses for the Weibull process with incomplete observations via classical approach are studied. Specifically, observations in the early developmental phase of a testing program cannot be observed. We derive the closed-form expressions for the maximum likelihood estimates of the parameters in both the failure- and time-truncated Weibull processes. Confidence interval and hypothesis testing for the parameters of interest are considered. In addition, predictive inferences on future failures and the goodness-of-fit test of the model are developed. Two real examples from an engine system development study and a Boeing air-conditioning system development study are presented to illustrate the proposed methodologies. © 2007 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/59883
ISSN
2015 Impact Factor: 1.179
2015 SCImago Journal Rankings: 1.283
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorYu, JWen_HK
dc.contributor.authorTian, GLen_HK
dc.contributor.authorTang, MLen_HK
dc.date.accessioned2010-05-31T03:59:22Z-
dc.date.available2010-05-31T03:59:22Z-
dc.date.issued2008en_HK
dc.identifier.citationComputational Statistics And Data Analysis, 2008, v. 52 n. 3, p. 1587-1603en_HK
dc.identifier.issn0167-9473en_HK
dc.identifier.urihttp://hdl.handle.net/10722/59883-
dc.description.abstractIn this article, statistical inference and prediction analyses for the Weibull process with incomplete observations via classical approach are studied. Specifically, observations in the early developmental phase of a testing program cannot be observed. We derive the closed-form expressions for the maximum likelihood estimates of the parameters in both the failure- and time-truncated Weibull processes. Confidence interval and hypothesis testing for the parameters of interest are considered. In addition, predictive inferences on future failures and the goodness-of-fit test of the model are developed. Two real examples from an engine system development study and a Boeing air-conditioning system development study are presented to illustrate the proposed methodologies. © 2007 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csdaen_HK
dc.relation.ispartofComputational Statistics and Data Analysisen_HK
dc.subjectAMSAA modelen_HK
dc.subjectConfidence intervalsen_HK
dc.subjectGoodness-of-fit testen_HK
dc.subjectNonhomogeneous Poisson processen_HK
dc.subjectPrediction limitsen_HK
dc.subjectReliability growthen_HK
dc.subjectWeibull processen_HK
dc.titleStatistical inference and prediction for the Weibull process with incomplete observationsen_HK
dc.typeArticleen_HK
dc.identifier.emailTian, GL: gltian@hku.hken_HK
dc.identifier.authorityTian, GL=rp00789en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.csda.2007.05.003en_HK
dc.identifier.scopuseid_2-s2.0-35549002651en_HK
dc.identifier.hkuros163562en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-35549002651&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume52en_HK
dc.identifier.issue3en_HK
dc.identifier.spage1587en_HK
dc.identifier.epage1603en_HK
dc.identifier.isiWOS:000253669700025-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridYu, JW=16204381100en_HK
dc.identifier.scopusauthoridTian, GL=25621549400en_HK
dc.identifier.scopusauthoridTang, ML=7401974011en_HK

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