The researchers developed a new method to improve the accuracy of estimating parameters in a nonlinear regression model with varying levels of error. They used robust estimators to handle outliers and prevent the estimates from becoming unreliable. By combining these robust estimators, they created a more accurate Extended Generalized Estimator for the model parameters.