Efficient maximin distance designs for experiments in mixtures

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dc.contributor.authorCoetzer R.L.J.
dc.contributor.authorRossouw R.F.
dc.contributor.authorLe Roux N.J.
dc.date.accessioned2012-08-17T12:21:18Z
dc.date.available2012-08-17T12:21:18Z
dc.date.issued2012
dc.descriptionArticle
dc.description.abstractIn 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.
dc.identifier.citationJournal of Applied Statistics
dc.identifier.citation39
dc.identifier.citation9
dc.identifier.citation1939
dc.identifier.citation1951
dc.identifier.issn2664763
dc.identifier.other10.1080/02664763.2012.697131
dc.identifier.urihttp://hdl.handle.net/10019.1/49247
dc.subjectcompositional data
dc.subjectcomputer experiments
dc.subjectdissimilarity measures
dc.subjectKullback-Leibler information
dc.subjectmaximin designs
dc.titleEfficient maximin distance designs for experiments in mixtures
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