Date of Award
Doctor of Philosophy
Dr. Joseph W. McKean
Dr. Joshua D. Naranjo
Dr. Gerald L. Sievers
Dr. Daniel P. Mihalko
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.
Abebe, Ashebar, "Nonlinear Regression Based on Ranks" (2002). Dissertations. 1153.
Fifth Advisor: Dr. Bradley E. Huitema