Date of Award

6-2002

Degree Name

Doctor of Philosophy

Department

Statistics

First Advisor

Dr. Joseph W. McKean

Second Advisor

Dr. Joshua D. Naranjo

Third Advisor

Dr. Gerald L. Sievers

Fourth Advisor

Dr. Daniel P. Mihalko

Abstract

This study presents robust methods for estimating parameters of nonlinear regression models. The proposed methods obtain estimates by minimizing rankbased dispersions instead of the Euclidean norm. We focus on the Wilcoxon and generalized signed-rank dispersion functions. Asymptotic properties of the estimators are established under mild regularity conditions similar to those used in least squares and least absolute deviations estimation. The study also shows that by considering the generalized signed-rank dispersion we obtain a class of estimators that encompasses most of the existing popular nonlinear regression estimators. As in linear models, these rank-based procedures provide estimators that are highly efficient. This fact is further confirmed for finite samples via a simulation study. Examples illustrating the robustness of the procedure are presented.

Comments

Fifth Advisor: Dr. Bradley E. Huitema

Access Setting

Dissertation-Open Access

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