dc.contributor | Universidade Estadual Paulista (Unesp) | |
dc.date.accessioned | 2014-05-27T11:23:58Z | |
dc.date.available | 2014-05-27T11:23:58Z | |
dc.date.created | 2014-05-27T11:23:58Z | |
dc.date.issued | 2009-09-01 | |
dc.identifier | Pesquisa Operacional, v. 29, n. 3, p. 547-562, 2009. | |
dc.identifier | 0101-7438 | |
dc.identifier | 1678-5142 | |
dc.identifier | http://hdl.handle.net/11449/71125 | |
dc.identifier | 10.1590/S0101-74382009000300005 | |
dc.identifier | S0101-74382009000300005 | |
dc.identifier | 2-s2.0-77955602950 | |
dc.identifier | 2-s2.0-77955602950.pdf | |
dc.description.abstract | The T2 chart and the generalized variance |S| chart are the usual tools for monitoring the mean vector and the covariance matrix of multivariate processes. The main drawback of these charts is the difficulty to obtain and to interpret the values of their monitoring statistics. In this paper, we study control charts for monitoring bivariate processes that only requires the computation of sample means (the ZMAX chart) for monitoring the mean vector, sample variances (the VMAX chart) for monitoring the covariance matrix, or both sample means and sample variances (the MCMAX chart) in the case of the joint control of the mean vector and the covariance matrix. | |
dc.language | eng | |
dc.relation | Pesquisa Operacional | |
dc.relation | 0,365 | |
dc.rights | Acesso aberto | |
dc.source | Scopus | |
dc.subject | Bivariate processes | |
dc.subject | Control charts | |
dc.subject | Covariance matrix | |
dc.subject | Mean vector | |
dc.title | Monitoring bivariate processes | |
dc.type | Artículos de revistas | |