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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/9499
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dc.contributor.authorCosta, Antonio F. B.-
dc.contributor.authorMachado, Marcela 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.issued2008-01-01-
dc.identifierhttp://dx.doi.org/10.1080/03610910801988987-
dc.identifier.citationCommunications In Statistics-simulation and Computation. Philadelphia: Taylor & Francis Inc, v. 37, n. 7, p. 1453-1465, 2008.-
dc.identifier.issn0361-0918-
dc.identifier.urihttp://hdl.handle.net/11449/9499-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/9499-
dc.description.abstractIn this article we consider a control chart based on the sample variances of two quality characteristics. The points plotted on the chart correspond to the maximum value of these two statistics. The main reason to consider the proposed chart instead of the generalized variance |S| chart is its better diagnostic feature, that is, with the new chart it is easier to relate an out-of-control signal to the variables whose parameters have moved away from their in-control values. We study the control chart efficiency considering different shifts in the covariance matrix. In this way, we obtain the average run length (ARL) that measures the effectiveness of a control chart in detecting process shifts. The proposed chart always detects process disturbances faster than the generalized variance |S| chart. The same is observed when the size of the samples is variable, except in a few cases in which the size of the samples switches between small size and very large size.en
dc.format.extent1453-1465-
dc.language.isoeng-
dc.publisherTaylor & Francis Inc-
dc.sourceWeb of Science-
dc.subjectbivariate processesen
dc.subjectcontrol charts for monitoring the covariance matrixen
dc.subjectgeneralized variance vertical bar S vertical bar charten
dc.subjectvariable sample sizeen
dc.titleA new chart for monitoring the covariance matrix of bivariate processesen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationUNESP, Fac Engn, Dept Prod, BR-12516410 Guaratingueta, SP, Brazil-
dc.description.affiliationUnespUNESP, Fac Engn, Dept Prod, BR-12516410 Guaratingueta, SP, Brazil-
dc.identifier.doi10.1080/03610910801988987-
dc.identifier.wosWOS:000258267900014-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofCommunications In Statistics-simulation and Computation-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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