完整後設資料紀錄
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dc.contributor.authorWu, Chien-Weien_US
dc.contributor.authorShu, Ming-Hungen_US
dc.contributor.authorPearn, W. L.en_US
dc.contributor.authorCheng, Feng-Tsungen_US
dc.date.accessioned2014-12-08T15:10:25Z-
dc.date.available2014-12-08T15:10:25Z-
dc.date.issued2009en_US
dc.identifier.issn0094-9655en_US
dc.identifier.urihttp://hdl.handle.net/11536/7949-
dc.identifier.urihttp://dx.doi.org/10.1080/00949650802140711en_US
dc.description.abstractThe process capability index Cpm, sometimes called the loss-based index, has been proposed to the manufacturing industry for measuring process reproduction capability. This index incorporates the variation of production items with respect to the target value and the specification limits preset in the factory. To estimate the loss-based index properly and accurately, certain frequentist and Bayesian perspectives have been proposed to obtain lower confidence bounds (LCBs) for providing minimum process capability. The LCBs not only provide critical information regarding process performance but are also used to determine whether an improvement was made in a capability index and by extension in reducing the fraction of non-conforming items. In this paper, under the assumption of normality, based on frequentist and Bayesian senses, several existing approaches for constructing LCBs of Cpm are presented. Depending on the statistical methods used, we then classify these existing approaches into three categories and compared them in terms of the coverage rates and the mean values of the LCBs via simulations. The relative advantages and disadvantages of these approaches are summarized with some highlights of the relevant findings.en_US
dc.language.isoen_USen_US
dc.subjectcoverage rateen_US
dc.subjectloss-based capability indexen_US
dc.subjectlower confidence bounden_US
dc.subjectperformance comparisonen_US
dc.titleA comparison of methods for estimating loss-based capability indexen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/00949650802140711en_US
dc.identifier.journalJOURNAL OF STATISTICAL COMPUTATION AND SIMULATIONen_US
dc.citation.volume79en_US
dc.citation.issue9en_US
dc.citation.spage1129en_US
dc.citation.epage1141en_US
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000270155600005-
dc.citation.woscount2-
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