Another look at Huberʹs estimator: A new minimax estimator in regression with stochastically bounded noise

Huberʹs estimator has had a long lasting impact, particularly on robust statistics. It is well known that under certain conditions, Huberʹs estimator is asymptotically minimax. A moderate generalization in rederiving Huberʹs estimator shows that Huberʹs estimator is not the only choice. We develop an alternative asymptotic minimax estimator and name it regression with stochastically bounded noise (RSBN). Simulations demonstrate that RSBN is slightly better in performance, although it is unclear how to justify such an improvement theoretically. We propose two numerical solutions: an iterative numerical solution, which is extremely easy to implement and is based on the proximal point method; and a solution by applying state-of-the-art nonlinear optimization software packages, e.g., SNOPT. Contribution: the generalization of the variational approach is interesting and should be useful in deriving other asymptotic minimax estimators in other problems.