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Utilize este identificador para citar ou criar um link para este item: http://acervodigital.unesp.br/handle/11449/9497
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dc.contributor.authorCosta, A. F. B.-
dc.contributor.authorMachado, M. A. G.-
dc.date.accessioned2014-05-20T13:28:31Z-
dc.date.accessioned2016-10-25T16:48:12Z-
dc.date.available2014-05-20T13:28:31Z-
dc.date.available2016-10-25T16:48:12Z-
dc.date.issued2011-01-01-
dc.identifierhttp://dx.doi.org/10.1080/02664760903406413-
dc.identifier.citationJournal of Applied Statistics. Abingdon: Routledge Journals, Taylor & Francis Ltd, v. 38, n. 2, p. 233-245, 2011.-
dc.identifier.issn0266-4763-
dc.identifier.urihttp://hdl.handle.net/11449/9497-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/9497-
dc.description.abstractFor the univariate case, the R chart and the S(2) chart are the most common charts used for monitoring the process dispersion. With the usual sample size of 4 and 5, the R chart is slightly inferior to the S(2) chart in terms of efficiency in detecting process shifts. In this article, we show that for the multivariate case, the chart based on the standardized sample ranges, we call the RMAX chart, is substantially inferior in terms of efficiency in detecting shifts in the covariance matrix than the VMAX chart, which is based on the standardized sample variances. The user's familiarity with sample ranges is a point in favor of the RMAX chart. An example is presented to illustrate the application of the proposed chart.en
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
dc.format.extent233-245-
dc.language.isoeng-
dc.publisherRoutledge Journals, Taylor & Francis Ltd-
dc.sourceWeb of Science-
dc.subjectcontrol chartsen
dc.subjectmultivariate processesen
dc.subjectcovariance matrixen
dc.subjectsample rangeen
dc.subjectsample varianceen
dc.titleA control chart based on sample ranges for monitoring the covariance matrix of the multivariate processesen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationSão Paulo State Univ, UNESP, Dept Prod, BR-12516410 Guaratingueta, SP, Brazil-
dc.description.affiliationUnespSão Paulo State Univ, UNESP, Dept Prod, BR-12516410 Guaratingueta, SP, Brazil-
dc.description.sponsorshipIdCNPq: 307744/2006-0-
dc.description.sponsorshipIdFAPESP: 06/00491-0-
dc.identifier.doi10.1080/02664760903406413-
dc.identifier.wosWOS:000286976100002-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofJournal of Applied Statistics-
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