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.

Description: In this paper, we study a survival regression model known as Cox's proportional Hazards model. We assume that the data available are already grouped and develop a minimum distance estimation procedure that is also applicable when the data are also left or right censored. Asymptotic properties of the estimators are established and the numerical implementation of the method based on an iterated reweighted least squares algorithm is discussed.