Model-robust design of mixture experiments

Optimal designs are often used for constrained mixture experiments because of the irregular design spaces. For these experiments, the number of blends needed to fit standard linear models may be too large when considering second- or third-order terms. We present a computationally-tractable algorithm for generating model-robust mixture designs that exploits anticipated effect sparsity by using a set of models defined by a user-specified number of higher-order terms. We compared the model-robust designs with Bayesian-optimal designs, and the model-robust designs show an improved ability to either estimate realistic models or make predictions for mixture experiments.