Optimal Robust Design for Bounded Bias and Variance
Analysis
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.
Key Takeaways
- •The paper explores robust experimental design in the presence of model misspecification.
- •It focuses on minimizing variance under a bias constraint and minimizing bias under a variance constraint.
- •Minimax designs are shown to be solutions to both optimization problems.
- •This provides a unified approach to robust experimental design.
Reference
“Solutions to both problems are given by the minimax designs, with appropriately chosen values of their tuning constant.”