A Distributionally Robust Estimator that Dominates the Empirical Average

Abstract

We leverage the duality between risk-averse and distributionally robust optimization (DRO) to devise a distributionally robust estimator that strictly outperforms the empirical average for all probability distributions with negative excess kurtosis. The aforesaid estimator solves the ${\chi }^2-$robust mean squared error problem in closed form.