dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-20T13:28:31Z
dc.date.available2014-05-20T13:28:31Z
dc.date.created2014-05-20T13:28:31Z
dc.date.issued2011-01-01
dc.identifierJournal of Applied Statistics. Abingdon: Routledge Journals, Taylor & Francis Ltd, v. 38, n. 2, p. 233-245, 2011.
dc.identifier0266-4763
dc.identifierhttp://hdl.handle.net/11449/9497
dc.identifier10.1080/02664760903406413
dc.identifierWOS:000286976100002
dc.identifier6100382011052492
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.
dc.languageeng
dc.publisherRoutledge Journals, Taylor & Francis Ltd
dc.relationJournal of Applied Statistics
dc.relation0.699
dc.relation0,475
dc.rightsAcesso restrito
dc.sourceWeb of Science
dc.subjectcontrol charts
dc.subjectmultivariate processes
dc.subjectcovariance matrix
dc.subjectsample range
dc.subjectsample variance
dc.titleA control chart based on sample ranges for monitoring the covariance matrix of the multivariate processes
dc.typeArtículos de revistas


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