Jackknife-Based Estimators and Confidence Regions in Nonlinear Regression

The properties of several jackknife-based estimators are investigated in the context of the nonlinear regression model. These estimators are suggested to overcome lack of balance of the design and the nonlinearity of the model. It is shown that a reweighted estimator combined with a modified variance estimate provides a technique that is reasonably robust with respect to outliers, leverage points, and curvature effects. Several examples are presented to illustrate these properties.