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dc.contributor.advisorSmith, Paulen_US
dc.contributor.authorSong, Xinen_US
dc.date.accessioned2009-07-02T05:41:01Z
dc.date.available2009-07-02T05:41:01Z
dc.date.issued2009en_US
dc.identifier.urihttp://hdl.handle.net/1903/9147
dc.description.abstractConstruction of control charts for multivariate process dispersion is not as straightforward as for the process mean. Because of the complexity of out of control scenarios, a general method is not available. In this dissertation, we consider the problem of monitoring multivariate dispersion from two perspectives. First, we derive asymptotic approximations to the power of Nagao's test for the equality of a normal dispersion matrix to a given constant matrix under local and fixed alternatives. Second, we propose various unequally weighted sum of squares estimators for the dispersion matrix, particularly with exponential weights. The new estimators give more weights to more recent observations and are not exactly Wishart distributed. Satterthwaite's method is used to approximate the distribution of the new estimators. By combining these two techniques based on exponentially weighted sums of squares and Nagao's test, we are able to propose a new control scheme <bold>MTNT</bold>, which is easy to implement. The control limits are easily calculated since they only depend on the dimension of the process and the desired in control average run length. Our simulations show that compared with schemes based on the likelihood ratio test and the sample generalized variance, <bold>MTNT</bold> has the shortest out of control average run length for a variety of out of control scenarios, particularly when process variances increase.en_US
dc.format.extent1968265 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.titleA New Scheme for Monitoring Multivariate Process Dispersionen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentMathematicsen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pquncontrolledDispersion matrixen_US
dc.subject.pquncontrolledMonitor multivariate process dispersionen_US
dc.subject.pquncontrolledNagao's Testen_US
dc.subject.pquncontrolledStatistical Process Controlen_US


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