Optimal Robust Design for Bounded Bias and Variance

This paper addresses the problem of designing experiments that are robust to model misspecification. It focuses on two key optimization problems: minimizing variance subject to a bias bound, and minimizing bias subject to a variance bound. The paper's significance lies in demonstrating that minimax designs, which minimize the maximum integrated mean squared error, provide solutions to both of these problems. This offers a unified framework for robust experimental design, connecting different optimization goals.