Multivariate nonparametric control charts using small samples

Simple item page
dc.contributor.authorKapatou, Alexandraen
dc.contributor.committeechairReynolds, Marion R. Jr.en
dc.contributor.committeememberArnold, Jesse C.en
dc.contributor.committeememberHinkelmann, Klaus H.en
dc.contributor.committeememberPalettas, Panickos N.en
dc.contributor.committeememberTerrell, George R.en
dc.contributor.departmentStatisticsen
dc.date.accessioned2014-03-14T21:12:48Zen
dc.date.adate2008-06-06en
dc.date.available2014-03-14T21:12:48Zen
dc.date.issued1996-02-05en
dc.date.rdate2008-06-06en
dc.date.sdate2008-06-06en
dc.description.abstractThe problem under consideration is simultaneous monitoring of the means of two or more correlated variables of a process, by collecting a small fixed random sample at fixed time intervals. The target values are considered known, whereas the variance covariance matrix of the data must be estimated. A typical parametric chart to monitor this process would involve the assumption that the data follow a multivariate normal distribution. If this assumption is not reasonable or if it is difficult to verify, for example in a short production run, a multivariate control chart based on classical nonparametric statistics could be used. Control charts based on the sign and signed rank statistics are explored. Past sample information for each variable is retained through an exponentially weighted moving average statistic (EWMA) in order to increase the sensitivity of the charts to detect small shifts from the target. The properties of the charts are evaluated using simulation. Such charts are not distribution-free in the nonparametric sense, but they are more robust than the parametric equivalent chart because, among other reasons, they require only covariance estimates. Nonparametric charts are less efficient than the parametric equivalent chart if the measurements follow a normal distribution, but they improve significantly if the measurements follow a distribution with heavier tails.en
dc.description.degreePh. D.en
dc.format.extentviii, 214 leavesen
dc.format.mediumBTDen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-06062008-155730en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-06062008-155730/en
dc.identifier.urihttp://hdl.handle.net/10919/38138en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.haspartLD5655.V856_1996.K373.pdfen
dc.relation.isformatofOCLC# 34650140en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectnormal distributionen
dc.subject.lccLD5655.V856 1996.K373en
dc.titleMultivariate nonparametric control charts using small samplesen
dc.typeDissertationen
dc.type.dcmitypeTexten
thesis.degree.disciplineStatisticsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
LD5655.V856_1996.K373.pdf
Size:
17.18 MB
Format:
Adobe Portable Document Format
Description:
Download

Collections

Doctoral Dissertations