Robust Inference in Conditionally Linear Nonlinear Regression Models

We consider robust methods of likelihood and frequentist inference for the nonlinear parameter, say "&agr;", in conditionally linear nonlinear regression models. We derive closed-form expressions for robust conditional, marginal, profile and modified profile likelihood functions for "&agr;" under elliptically contoured data distributions. Next, we develop robust exact-F confidence intervals for "&agr;" and consider robust Fieller intervals for ratios of regression parameters in linear models. Several well-known examples are considered and Monte Carlo simulation results are presented. Copyright (c) 2007 Board of the Foundation of the Scandinavian Journal of Statistics..