Linear Mixed Model Robust Regression

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dc.contributor.authorWaterman, Megan Janet Tuttleen
dc.contributor.committeecochairBirch, Jeffrey B.en
dc.contributor.committeecochairSchabenberger, Oliveren
dc.contributor.committeememberAnderson-Cook, Christine M.en
dc.contributor.committeememberSmith, Eric P.en
dc.contributor.committeememberTerrell, George R.en
dc.contributor.departmentStatisticsen
dc.date.accessioned2014-03-14T20:12:01Zen
dc.date.adate2002-05-21en
dc.date.available2014-03-14T20:12:01Zen
dc.date.issued2002-05-08en
dc.date.rdate2003-05-21en
dc.date.sdate2002-05-14en
dc.description.abstractMixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric models, correctness of the assumed model is critical for the validity of the ensuing inference. Model robust regression techniques predict mean response as a convex combination of a parametric and a nonparametric model fit to the data. It is a semiparametric method by which incompletely or incorrectly specified parametric models can be improved through adding an appropriate amount of a nonparametric fit. We apply this idea of model robustness in the framework of the linear mixed model. The mixed model robust regression (MMRR) predictions we propose are convex combinations of predictions obtained from a standard normal-theory linear mixed model, which serves as the parametric model component, and a locally weighted maximum likelihood fit which serves as the nonparametric component. An application of this technique with real data is provided.en
dc.description.degreePh. D.en
dc.identifier.otheretd-05142002-133213en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05142002-133213/en
dc.identifier.urihttp://hdl.handle.net/10919/27708en
dc.publisherVirginia Techen
dc.relation.haspartdissertation.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectsemiparametricen
dc.subjectmixed effectsen
dc.subjectrobusten
dc.titleLinear Mixed Model Robust Regressionen
dc.typeDissertationen
thesis.degree.disciplineStatisticsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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