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. / Ph. D.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/30493 |
| Date | 14 April 1997 |
| Creators | Assaid, Christopher Ashley |
| Contributors | Statistics, Birch, Jeffrey B. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf, application/pdf, application/pdf, application/pdf, application/pdf, application/pdf, application/pdf, application/pdf, application/pdf, application/pdf, application/pdf, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | etd.PDF, Ch1.PDF, Ch2.PDF, Ch3.PDF, Ch4.PDF, Ch5.PDF, Ch6.PDF, Ch7.PDF, Ch8.PDF, ch9.pdf, ch10.pdf, Biblio.PDF, Appndx.PDF |
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