Alternative criteria for measuring estimator robustness

Intuitively pleasing criteria which allow the measurement of the robustness of an estimator are presented. Contrary to classical saddlepoint criteria, these methods are naturally quantitative and admit essentially arbitrary perturbations in the underlying joint distribution away from a nominal. The methods succeed by exploiting important geometric concepts such as curvature.<>