Title page for ETD etd-05142002-133213


Type of Document Dissertation
Author Waterman, Megan Janet Tuttle
Author's Email Address mwaterma@vt.edu
URN etd-05142002-133213
Title Linear Mixed Model Robust Regression
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
Smith, Eric P. Committee Member
Terrell, George R. Committee Member
Keywords
  • semiparametric
  • mixed effects
  • robust
Date of Defense 2002-05-08
Availability unrestricted
Abstract
Mixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric models, correctness of the assumed model is critical for the validity of the ensuing inference. Model robust regression techniques predict mean response as a convex combination of a parametric and a nonparametric model fit to the data. It is a semiparametric method by which incompletely or incorrectly specified parametric models can be improved through adding an appropriate amount of a nonparametric fit. We apply this idea of model robustness in the framework of the linear mixed model. The mixed model robust regression (MMRR) predictions we propose are convex combinations of predictions obtained from a standard normal-theory linear mixed model, which serves as the parametric model component, and a locally weighted maximum likelihood fit which serves as the nonparametric component. An application of this technique with real data is provided.
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