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dc.contributor.authorMingoti, Sueli Aparecida-
dc.contributor.authorOliveira, Fernando Luiz Pereira de-
dc.identifier.citationMINGOTI, S. A.; OLIVEIRA, F. L. P. de. On capability indices for multivariate autocorrelated processes. Brazilian Journal of Operations and Production Management, v. 8, n.1, p.133-152, 2011. Disponível em: <>. Acesso em: 30 nov. 2012.pt_BR
dc.description.abstractIn this paper the effects of the autocorrelation on some multivariate capability indices commonly used for independent processes are discussed and a correction is proposed. Some results are shown for VARMA(1,1) and VAR(1) time series processes under the multivariate normality assumption and the proportion of non-conforming units is calculated for some bivariate VAR(1) models. An extension of Veevers capability index for non-centered processes is also a subject addressed in this paper. An example of application in blast charcoal furnace pig iron process is presented and bootstrap is used to build confidence intervals for its true capability value as well as to evaluate the performance of the capability estimators. Similar as to what is already known for univariate processes the results showed that autocorrelation has a large impact in the multivariate capabilities indices. This paper also shows that some care should be taken when using Niverthi and Dey’s capabilities indices since they are very sensitive to any deviations from the process means to the specification means up to a point that a capable process might be considered non-capable.pt_BR
dc.subjectMultivariate time seriespt_BR
dc.subjectMultivariate capability indicespt_BR
dc.subjectAutocorrelated processespt_BR
dc.titleOn capability indices for multivariate autocorrelated processes.pt_BR
dc.typeArtigo publicado em periodicopt_BR
dc.rights.licenseDisponível sob Licença Creative Commons 3.0, que permite copiar, distribuir e transmitir o trabalho, desde que seja citado o autor e licenciante. Não permite o uso para fins comerciais nem a adaptação desta. Fonte: Brazilian Journal of Operations & Production Management <> Acesso em 25 nov. 2013.-
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