Constructing model robust mixture designs via weighted G-optimality criterion

Exchange Algorithms for Constructing Model-Robust Experimental Designs

S INCE Kiefer (1959) debuted the idea of optimal design of experiments, a vast literature has grown up around the notion of choosing a design based on some numerical criterion. The most common is D-optimality, which chooses the design minimizing the generalized variance of the regression parameter estimates. Though standard designs can be used in most design situations, optimal procedures are u...

Shrinkage simplex-centroid designs for a quadratic mixture model

A simplex-centroid design for q mixture components comprises of all possible subsets of the q components, present in equal proportions. The design does not contain full mixture blends except the overall centroid. In real-life situations, all mixture blends comprise of at least a minimum proportion of each component. Here, we introduce simplex-centroid designs which contain complete blend...

Designing experiments for robust-optimization problems: the Vs-optimality criterion

We suggest an experimentation strategy for the robust design of empirically fitted models. The suggested approach is used to design experiments that minimize the variance of the optimal robust solution. The new design-of-experiment optimality criterion, termed Vs-optimal, prioritizes the estimation of a model’s coefficients, such that the variance of the optimal solution is minimized by the per...

Model-Robust Factorial Designs

A Comparison of Three Approaches for Constructing Robust Experimental Designs

While optimal designs are commonly used in the design of experiments, the optimality of those designs frequently depends on the form of an assumed model. Several useful criteria have been proposed to reduce such dependence, and efficient designs have been then constructed based on the criteria, often algorithmically. In the model robust design paradigm, a space of possible models is specified a...