Defense Date


Document Type


Degree Name

Master of Science


Mathematical Sciences

First Advisor

James E. Mays


There has been extensive research done in the area of Semiparametric Regression. These techniques deliver substantial improvements over previously developed methods, such as Ordinary Least Squares and Kernel Regression. Two of these hybrid techniques: Model Robust Regression 1 (MRR1) and Model Robust Regression 2 (MRR2) require the choice of an appropriate bandwidth for smoothing and a mixing parameter that allows a portion of a nonparametric fit to be used in fitting a model that may be misspecifed by other regression methods. The current method of choosing the bandwidth and mixing parameter does not guarantee the optimal choices in either case. The immediate objective of the current work is to address this process of choosing the optimal bandwidth and mixing parameter and to examine the behavior of these estimates using 3D plots. The 3D plots allow us to examine how the semiparametric techniques: MRR1 and MRR2, behave for the optimal (AVEMSE) selection process when compared to data-driven selectors, such as PRESS* and PRESS**. It was found that the structure of MRR2 behaved consistently under all conditions. MRR2 displayed a wider range of "acceptable" values for the choice of bandwidth as opposed to a much more limited choice when using MRR1. These results provide general support for earlier fndings by Mays et al. (2000).


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Date of Submission

August 2009