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Statistical process monitoring using an empirical Bayes multivariate process control chart

dc.citation.epage124en_US
dc.citation.issue2en_US
dc.citation.spage119en_US
dc.citation.volume17en_US
dc.citation.woscount3
dc.contributor.authorFeltz, CJen_US
dc.contributor.authorShiau, JJHen_US
dc.contributor.department統計學研究所zh_TW
dc.contributor.departmentInstitute of Statisticsen_US
dc.date.accessioned2014-12-08T15:44:09Z
dc.date.available2014-12-08T15:44:09Z
dc.date.issued2001-03-01en_US
dc.description.abstractIn this paper, we describe the theory underlying an empirical Bayesian approach to monitoring two or more process characteristics simultaneously. If the, data is continuous and multivariate in nature, often the multivariate normal distribution can be used to model the process. Then, using Bayesian theory: we develop techniques to implement empirical Bayes process monitoring of the multivariable process. Lastly, an example is given to illustrate the use of our techniques. Copyright (C) 2001 John Wiley & Sons, Ltd.en_US
dc.identifier.doi10.1002/qre.393en_US
dc.identifier.issn0748-8017en_US
dc.identifier.journalQUALITY AND RELIABILITY ENGINEERING INTERNATIONALen_US
dc.identifier.urihttp://dx.doi.org/10.1002/qre.393en_US
dc.identifier.urihttps://ir.lib.nycu.edu.tw/handle/11536/29810
dc.identifier.wosnumberWOS:000168447800007
dc.language.isoen_USen_US
dc.subjectmultivariateen_US
dc.subjectquality controlen_US
dc.subjecton-lineen_US
dc.titleStatistical process monitoring using an empirical Bayes multivariate process control charten_US
dc.typeArticleen_US

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