A Bayesian Hierarchical Approach to Dual Response Surface Modeling

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dc.contributor.authorChen, Younanen
dc.contributor.authorYe, Keyingen
dc.contributor.departmentStatisticsen
dc.date.accessioned2019-05-08T19:46:22Zen
dc.date.available2019-05-08T19:46:22Zen
dc.date.issued2005en
dc.description.abstractIn modern quality engineering, dual response surface methodology is a powerful tool to monitor an industrial process by using both the mean and the standard deviation of the measurements as the responses. The least squares method in regression is often used to estimate the coefficients in the mean and standard deviation models, and various decision criteria are proposed by researchers to find the optimal conditions. Based on the inherent hierarchical structure of the dual response problems, we propose a hierarchical Bayesian approach to model dual response surfaces. Such an approach is compared with two frequentist least squares methods by using two real data sets and simulated data.en
dc.format.extent24 pagesen
dc.format.mimetypeapplication/pdfen
dc.identifier.sourceurlhttps://www.stat.vt.edu/content/dam/stat_vt_edu/graphics-and-pdfs/research-papers/Technical_Reports/TechReport05-5.pdfen
dc.identifier.urihttp://hdl.handle.net/10919/89426en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.ispartofseriesTechnical Report No. 05-5en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectBayesian hierarchical modelen
dc.subjectdual response surfaceen
dc.subjectoff-line quality controlen
dc.subjectgenetic algorithmen
dc.subjectoptimizationen
dc.titleA Bayesian Hierarchical Approach to Dual Response Surface Modelingen
dc.typeTechnical reporten
dc.type.dcmitypeTexten

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