Nonlinear regression models are used in fields like toxicology/pharmacology, where error variance structure affects parameter estimation. A robust M-estimation method was studied for handling outliers in nonlinear regression with varying error variances. Three variance models were compared using simulation, showing that a weighted M-estimator with a nonlinear variance model performs well for both homoscedastic and heteroscedastic data. The method was validated using real toxicological data.