{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/30493"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/30493","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Outlier Resistant Model Robust Regression","abstract":"Parametric 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.","abstract_html":"Parametric 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.","abstract_has_math":false,"creators":["Assaid, Christopher Ashley"],"institution":"Virginia Tech","degree_name":"Ph. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph. D."]},{"key":"dc:title","label":"Title","values":["Outlier Resistant Model Robust Regression"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Birch, Jeffrey B."],"dc:contributor.department":["Statistics"],"dc:creator":["Assaid, Christopher Ashley"],"dc:date.accessioned":["2014-03-14T20:21:52Z"],"dc:date.available":["2014-03-14T20:21:52Z","2006-01-17"],"dc:date.issued":["1997-04-14"],"dc:description.abstract":["Parametric 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. 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