Efficient maximin distance designs for experiments in mixtures
Date
2012
Authors
Coetzer R.L.J.
Rossouw R.F.
Le Roux N.J.
Journal Title
Journal ISSN
Volume Title
Publisher
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. © 2012 Copyright Taylor and Francis Group, LLC.
Description
Article
Keywords
compositional data, computer experiments, dissimilarity measures, Kullback-Leibler information, maximin designs
Citation
Journal of Applied Statistics
39
9
1939
1951
39
9
1939
1951