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dc.contributor.advisorChitturi, Pallavi
dc.creatorCheng, Aili
dc.date.accessioned2020-10-21T14:27:03Z
dc.date.available2020-10-21T14:27:03Z
dc.date.issued2012
dc.identifier.other864885823
dc.identifier.urihttp://hdl.handle.net/20.500.12613/959
dc.description.abstractIn robust parameter design it is often possible to set the levels of the controllable factors to produce a zero gradient for the transmission of variability from the noise variables. If the number of control variables is greater than the number of noise variables, a continuum of zero-gradient solutions exists. This situation is useful as it provides the experimenter with multiple conditions under which to configure a zero gradient for noise variable transmission. However, this situation requires a confidence region for the multiple-solution factor levels that provides proper simultaneous coverage. This requirement has not been previously recognized in the literature. In the case where the number of control variables is greater than the number of noise variables, we show how to construct critical values needed to maintain the simultaneous coverage rate. Two examples are provided as a demonstration of the practical need to adjust the critical values for simultaneous coverage. The zero-gradient confidence region only focuses on the variance, and there are in fact many such situations in which focus is or could be placed entirely on the process variance. In the situation where both mean and variance need to be considered, a general confidence region in control variables is developed by minimizing weighted mean square error. This general method is applicable to many situations including mixture experiments which have an inherit constraint on the control factors. It also gives the user the flexibility to put different weights on the mean and variance parts for simultaneous optimization. It turns out that the same computational algorithm can be used to compute the dual confidence region in both control factors and the response variable.
dc.format.extent129 pages
dc.language.isoeng
dc.publisherTemple University. Libraries
dc.relation.ispartofTheses and Dissertations
dc.rightsIN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available.
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectStatistics
dc.subjectConfidence Region
dc.subjectNoise Variables
dc.subjectResponse Surface
dc.subjectRobust Parameter Design
dc.titleCONFIDENCE REGIONS FOR OPTIMAL CONTROLLABLE VARIABLES FOR THE ROBUST PARAMETER DESIGN PROBLEM
dc.typeText
dc.type.genreThesis/Dissertation
dc.contributor.committeememberRaghavarao, Damaraju
dc.contributor.committeememberSobel, Marc J.
dc.contributor.committeememberPeterson, John
dc.contributor.committeememberViswanathan, Krupa S.
dc.description.departmentStatistics
dc.relation.doihttp://dx.doi.org/10.34944/dspace/941
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.description.degreePh.D.
refterms.dateFOA2020-10-21T14:27:03Z


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