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dc.contributor.authorOzol-Godfrey, Aycaen_US
dc.date.accessioned2014-03-14T20:20:58Z
dc.date.available2014-03-14T20:20:58Z
dc.date.issued2004-12-15en_US
dc.identifier.otheretd-12202004-205209en_US
dc.identifier.urihttp://hdl.handle.net/10919/30185
dc.description.abstractGraphical summaries are becoming important tools for evaluating designs. The need to compare designs in term of their prediction variance properties advanced this development. A recent graphical tool, the Fraction of Design Space plot, is useful to calculate the fraction of the design space where the scaled prediction variance (SPV) is less than or equal to a given value. In this dissertation we adapt FDS plots, to study three specific design problems: robustness to model assumptions, robustness to measurement error and design properties for generalized linear models (GLM). This dissertation presents a graphical method for examining design robustness related to the SPV values using FDS plots by comparing designs across a number of potential models in a pre-specified model space. Scaling the FDS curves by the G-optimal bounds of each model helps compare designs on the same model scale. FDS plots are also adapted for comparing designs under the GLM framework. Since parameter estimates need to be specified, robustness to parameter misspecification is incorporated into the plots. Binomial and Poisson examples are used to study several scenarios. The third section involves a special type of response surface designs, mixture experiments, and deals with adapting FDS plots for two types of measurement error which can appear due to inaccurate measurements of the individual mixture component amounts. The last part of the dissertation covers mixture experiments for the GLM case and examines prediction properties of mixture designs using the adapted FDS plots.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartdissertation.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectFDS Plotsen_US
dc.subjectDesign Optimalityen_US
dc.subjectGeneralized Linear Modelsen_US
dc.titleUnderstanding Scaled Prediction Variance Using Graphical Methods for Model Robustness, Measurement Error and Generalized Linear Models for Response Surface Designsen_US
dc.typeDissertationen_US
dc.contributor.departmentStatisticsen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineStatisticsen_US
dc.contributor.committeechairAnderson-Cook, Christine M.en_US
dc.contributor.committeememberWoodall, William H.en_US
dc.contributor.committeememberYe, Keyingen_US
dc.contributor.committeememberVining, G. Geoffreyen_US
dc.contributor.committeememberSmith, Eric P.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-12202004-205209/en_US
dc.date.sdate2004-12-20en_US
dc.date.rdate2004-12-23
dc.date.adate2004-12-23en_US


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