A perturbation scheme for nonlinear models

In nonlinear regression, we measure the interaction between observations in a random perturbation model for assessing the local influence. Our perturbation model perturbs all cases separately, and our measures combine all sides together. Approximations are given for these measures. An example of a nonlinear model shows the effectiveness of these measures when masking exists. This perturbation scheme has proved useful in applications beyond the scope of this paper.