Estimation of Pareto Survival Function in the Presence of Outlying Observations

In the present paper we discuss the problem of estimating the survival function R(x) = P(X > x) of the Pareto distribution, when the sample contains discordant observations. Bayes point estimates and credible intervals are obtained by assuming exchangeable and identifiable models and by discarding the outlying observations under a class of loss functions. We illustrate the estimators with a real data-set and then compare the three estimators using a simulation study.