A new type of robust designs for chemometrics and computer experiments

There are many difficulties that may be encountered in experiments of chemometrics as well as most computer experiments, that ought to explore an approximate model instead of the true one that is complicated. Space-filling designs including uniform designs are robust for such situation. However, when the underlying regression model is known, the D-optimal design (DOD) is the most effective on parameter estimation, but DOD is not robust against the model change. In this paper, we propose a new type of composite designs guaranteeing both robustness and effectiveness. Subsequently, we compare the prediction performance of the seven candidate composite designs, under various case studies. For the convenience of implementation, instead of chemical experiments, we adopt computer experiments as illustration involving some popular models that have been widely applied in evaluating the performance of optimization algorithms. Among all candidate composite designs, we recommend two of them based on their advantaged performance in all cases we explored. Our recommendation is also suitable for most physical experiments.