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dc.contributor.authorAssaid, Christopher Ashleyen
dc.date.accessioned2014-03-14T20:21:52Zen
dc.date.available2014-03-14T20:21:52Zen
dc.date.issued1997-04-14en
dc.identifier.otheretd-3649212139711101en
dc.identifier.urihttp://hdl.handle.net/10919/30493en
dc.description.abstractParametric regression fitting (such as OLS) to a data set requires specification of an underlying model. If the specified model is different from the true model, then the parametric fit suffers to a degree that varies with the extent of model misspecification. Mays and Birch (1996) addressed this problem in the one regressor variable case with a method known as Model Robust Regression (MRR), which is a weighted average of independent parametric and nonparametric fits to the data. This paper was based on the underlying assumption of "well-behaved" (Normal) data. The method seeks to take advantage of the beneficial aspects of the both techniques: the parametric, which makes use of the prior knowledge of the researcher via a specified model, and the nonparametric, which is not restricted by a (possibly misspecified) underlying model. The method introduced here (termed Outlier Resistant Model Robust Regression (ORMRR)) addresses the situation that arises when one cannot assume well-behaved data that vary according to a Normal distribution. ORMRR is a blend of a robust parametric fit, such as M-estimation, with a robust nonparametric fit, such as Loess. Some properties of the method will be discussed as well as illustrated with several examples.en
dc.publisherVirginia Techen
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dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectregressionen
dc.titleOutlier Resistant Model Robust Regressionen
dc.typeDissertationen
dc.contributor.departmentStatisticsen
dc.description.degreePh. D.en
thesis.degree.namePh. D.en
thesis.degree.leveldoctoralen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineStatisticsen
dc.contributor.committeechairBirch, Jeffrey B.en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-3649212139711101/en
dc.date.sdate1998-07-25en
dc.date.rdate2006-01-17en
dc.date.adate1997-04-14en


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