Approximate Model Spaces for Model-Robust Experiment Design *

Abstract Optimal designs depend upon a prespecified model form. A popular and effective modelrobust alternative is to design with respect to a set of models instead of just one. However, model spaces associated with experiments of interest are often prohibitively large and so algorithmically-generated designs are infeasible. Here, we present a simple method which largely eliminates this problem by choosing a small set of models which approximates the full set and finding designs that are explicitly robust for this small set. We build our procedure on a restricted columnwise-pairwise algorithm, and explore its effectiveness for two model spaces in the literature. For smaller full model spaces, we find that the designs constructed with the new method compare favorably to robust designs which utilize the full model space, with construction times smaller by orders of magnitude. We also construct designs that heretofore have been unobtainable due to the size of their model spaces. Supplementary material (available online) includes code, designs, and additional results