Title page for ETD etd-04022002-161238


Type of Document Dissertation
Author Clark, Seth K.
URN etd-04022002-161238
Title Model Robust Regression Based on Generalized Estimating Equations
Degree PhD
Department Statistics
Advisory Committee
Advisor Name Title
Birch, Jeffrey B. Committee Co-Chair
Schabenberger, Oliver Committee Co-Chair
Anderson-Cook, Christine M. Committee Member
Terrell, George R. Committee Member
Ye, Keying Committee Member
Keywords
  • Local Model
  • Nonparametric Regression
  • Semiparametric
  • Model Misspecification
Date of Defense 2002-03-29
Availability unrestricted
Abstract
One form of model robust regression (MRR) predicts mean response as a convex

combination of a parametric and a nonparametric prediction. MRR is a semiparametric

method by which an incompletely or an incorrectly specified parametric model can be

improved through adding an appropriate amount of a nonparametric fit. The combined

predictor can have less bias than the parametric model estimate alone and less

variance than the nonparametric estimate alone. Additionally, as shown in previous

work for uncorrelated data with linear mean function, MRR can converge faster than the

nonparametric predictor alone. We extend the MRR technique to the problem of

predicting mean response for clustered non-normal data. We combine a nonparametric

method based on local estimation with a global, parametric generalized estimating

equations (GEE) estimate through a mixing parameter on both the mean scale and the

linear predictor scale. As a special case, when data are uncorrelated, this amounts to

mixing a local likelihood estimate with predictions from a global generalized linear

model. Cross-validation bandwidth and optimal mixing parameter selectors are

developed. The global fits and the optimal and data-driven local and mixed fits are

studied under no/some/substantial model misspecification via simulation. The methods

are then illustrated through application to data from a longitudinal study.

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