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.

由于设计空间不规则，通常将最佳设计用于受限混合实验。对于这些实验，在考虑二阶或三阶项时，拟合标准线性模型所需的混合数量可能太大。我们提出了一种计算可处理的算法，用于生成模型稳健的混合物设计，该模型通过使用由用户指定数量的高阶项定义的一组模型来利用预期的效果稀疏性。我们将模型鲁棒性设计与贝叶斯最优设计进行了比较，模型鲁棒性设计显示了更高的估计实际模型或为混合实验做出预测的能力。