Optimal unbiased estimators via convex hulls

Abstract

Necessary and sufficient conditions for the square-integrability of recently proposed unbiased estimators are established. A geometric characterization of a distribution that optimizes the performance of these estimators is given. An algorithm based on convex hulls that finds the optimal distribution truncated to its first m terms in time linear in m is described. The algorithm exploits a connection with a recent randomized dimension reduction method and is illustrated via a numerical example.