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dc.contributor.authorStarnes, Brett Aldenen_US
dc.date.accessioned2014-03-14T20:21:07Z
dc.date.available2014-03-14T20:21:07Z
dc.date.issued1999-12-14en_US
dc.identifier.otheretd-122299-184429en_US
dc.identifier.urihttp://hdl.handle.net/10919/30244
dc.description.abstractSince the mid 1980's many statisticians have studied methods for combining parametric and nonparametric esimates to improve the quality of fits in a regression problem. Notably in 1987, Einsporn and Birch proposed the Model Robust Regression estimate (MRR1) in which estimates of the parametric function, f, and the nonparametric function, g, were combined in a straightforward fashion via the use of a mixing parameter, l. This technique was studied extensively at small samples and was shown to be quite effective at modeling various unusual functions. In 1995, Mays and Birch developed the MRR2 estimate as an alternative to MRR1. This model involved first forming the parametric fit to the data, and then adding in an estimate of g according to the lack of fit demonstrated by the error terms. Using small samples, they illustrated the superiority of MRR2 to MRR1 in most situations. In this dissertation we have developed asymptotic convergence rates for both MRR1 and MRR2 in OLS and GLS (maximum likelihood) settings. In many of these settings, it is demonstrated that the user of MRR1 or MRR2 achieves the best convergence rates available regardless of whether or not the model is properly specified. This is the "Golden Result of Model Robust Regression". It turns out that the selection of the mixing parameter is paramount in determining whether or not this result is attained.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartS7994Ch4b.PDFen_US
dc.relation.haspartS7994x4a.PDFen_US
dc.relation.haspartS7994Ch6.PDFen_US
dc.relation.haspartS7994rb.pdfen_US
dc.relation.haspartS7994x3a.PDFen_US
dc.relation.haspartS7994x3b.PDFen_US
dc.relation.haspartS7994x3c.PDFen_US
dc.relation.haspartS7994Ch4a.PDFen_US
dc.relation.haspartS7994Ch3c.PDFen_US
dc.relation.haspartS7994Vita.PDFen_US
dc.relation.haspartS7994x5b.PDFen_US
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dc.relation.haspartS7994Ch3b.PDFen_US
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dc.relation.haspartS7994Ch5b.PDFen_US
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dc.relation.haspartS7994Ch2b.PDFen_US
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dc.relation.haspartS7994Ch5a.PDFen_US
dc.rightsI hereby grant to Virginia Tech or its agents the right to archive and to make available my thesis or dissertation in whole or in part in the University Libraries in all forms of media, now or hereafter known. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation.en_US
dc.subjectMixing Parameteren_US
dc.subjectNonparametricen_US
dc.subjectParametricen_US
dc.subjectAsymptoticen_US
dc.subjectConvergence Ratesen_US
dc.subjectRegressionen_US
dc.subjectSemiparametricen_US
dc.titleAsymptotic Results for Model Robust Regressionen_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.committeechairBirch, Jeffrey B.en_US
dc.contributor.committeememberAnderson-Cook, Christine M.en_US
dc.contributor.committeememberSmith, Eric P.en_US
dc.contributor.committeememberCoakley, Clint W.en_US
dc.contributor.committeememberTerrell, George R.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-122299-184429/en_US
dc.date.sdate1999-12-22en_US
dc.date.rdate2004-04-11
dc.date.adate1999-12-31en_US


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