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Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0167-9473(02)00232-3
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dc.titleTransformation approaches for the construction of Weibull prediction interval
dc.contributor.authorYang, Z.
dc.contributor.authorSee, S.P.
dc.contributor.authorXie, M.
dc.date.accessioned2014-06-17T07:03:25Z
dc.date.available2014-06-17T07:03:25Z
dc.date.issued2003-07-28
dc.identifier.citationYang, Z., See, S.P., Xie, M. (2003-07-28). Transformation approaches for the construction of Weibull prediction interval. Computational Statistics and Data Analysis 43 (3) : 357-368. ScholarBank@NUS Repository. https://doi.org/10.1016/S0167-9473(02)00232-3
dc.identifier.issn01679473
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/63384
dc.description.abstractTwo methods of transforming the Weibull data to near normality, namely the Box-Cox method and Kullback-Leibler (KL) information method, are discussed and contrasted. A simple prediction interval (PI) based on the better KL information method is proposed. The asymptotic property of this interval is established. Its small sample behavior is investigated using Monte Carlo simulation. Simulation results show that this simple interval is close to the existing complicated PI where the percentage points of the reference distribution have to be either simulated or approximated. The proposed interval can also be easily adjusted to have the correct asymptotic coverage. © 2002 Elsevier Science B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/S0167-9473(02)00232-3
dc.sourceScopus
dc.subjectBox-Cox transformation
dc.subjectCoverage probability
dc.subjectKullback-Leibler information
dc.subjectPrediction interval
dc.subjectWeibull distribution
dc.typeArticle
dc.contributor.departmentINDUSTRIAL & SYSTEMS ENGINEERING
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1016/S0167-9473(02)00232-3
dc.description.sourcetitleComputational Statistics and Data Analysis
dc.description.volume43
dc.description.issue3
dc.description.page357-368
dc.description.codenCSDAD
dc.identifier.isiut000183973200006
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