Before revision: We study robustness properties of several procedures for joint estimation of shape and scale in a generalized Pareto model. The estimators we primarily focus on, MBRE and OMSE, are one-step estimators distinguished as optimally-robust in the shrinking neighborhood setting, i. e.; they minimize the maximal bias, respectively, on a specific such neighborhood, the maximal mean squared error. For their initialization, we propose a particular Location-Dispersion (LD) estimator, kMedMAD, which matches the population median and kMAD (an asymmetric variant of the median of absolute deviations ) against the empirical counterparts. These optimally-robust estimators are compared to maximum likelihood, skipped maximum likelihood, Cramer-von-Mises minimum distance, method of median, and Pickands estimators.
After revision: This paper deals with optimally-robust estimators are compared to maximum likelihood, skipped maximum likelihood, Cramer-von-Mises minimum distance, method of median, and Pickands estimators.