dc.contributor.author | Assaid, Christopher Ashley | en |
dc.date.accessioned | 2014-03-14T20:21:52Z | en |
dc.date.available | 2014-03-14T20:21:52Z | en |
dc.date.issued | 1997-04-14 | en |
dc.identifier.other | etd-3649212139711101 | en |
dc.identifier.uri | http://hdl.handle.net/10919/30493 | en |
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. 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.publisher | Virginia Tech | en |
dc.relation.haspart | etd.PDF | en |
dc.relation.haspart | Ch1.PDF | en |
dc.relation.haspart | Ch2.PDF | en |
dc.relation.haspart | Ch3.PDF | en |
dc.relation.haspart | Ch4.PDF | en |
dc.relation.haspart | Ch5.PDF | en |
dc.relation.haspart | Ch6.PDF | en |
dc.relation.haspart | Ch7.PDF | en |
dc.relation.haspart | Ch8.PDF | en |
dc.relation.haspart | ch9.pdf | en |
dc.relation.haspart | ch10.pdf | en |
dc.relation.haspart | Biblio.PDF | en |
dc.relation.haspart | Appndx.PDF | en |
dc.rights | In Copyright | en |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
dc.subject | regression | en |
dc.title | Outlier Resistant Model Robust Regression | en |
dc.type | Dissertation | en |
dc.contributor.department | Statistics | en |
dc.description.degree | Ph. D. | en |
thesis.degree.name | Ph. D. | en |
thesis.degree.level | doctoral | en |
thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
thesis.degree.discipline | Statistics | en |
dc.contributor.committeechair | Birch, Jeffrey B. | en |
dc.identifier.sourceurl | http://scholar.lib.vt.edu/theses/available/etd-3649212139711101/ | en |
dc.date.sdate | 1998-07-25 | en |
dc.date.rdate | 2006-01-17 | en |
dc.date.adate | 1997-04-14 | en |