Description: Because actuaries fit models for a variety of situations, particularly with regard to data modifications, it is useful to have a single approach. Though not optimal or exact for a particular model or data structure, the method should be reasonable for most all settings. Such a method is proposed in this article. Actuarial Research Clearing House 2003, VOL. 1

Description: This paper considers the problem of parametric estimation of loss distributions in a very general context. It uses the minimum distance method with the Cramér-von Mises statistic as the particular choice of distance. The authors show how to compute the influence function for the estimator. They also use the influence function to obtain more information about the variance and the robustness of the estimator. They demonstrate, with an example, how much more resistant to contamination this estimator is compared with the more classical maximum likelihood estimator.