Efficient maximin distance designs for experiments in mixtures

Abstract

In this paper, different dissimilarity measures are investigated to construct maximin designs for compositional data. Specifically, the effect of different dissimilarity measures on the maximin design criterion for two case studies is presented. Design evaluation criteria are proposed to distinguish between the maximin designs generated. An optimization algorithm is also presented. Divergence is found to be the best dissimilarity measure to use in combination with the maximin design criterion for creating space-filling designs for mixture variables.

Keywords:

• compositional data
• computer experiments
• dissimilarity measures
• Kullback–Leibler information
• maximin designs