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Efficient maximin distance designs for experiments in mixtures

Author

Listed:
  • R. L.J. Coetzer
  • R. F Rossouw
  • N. J. Le Roux

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.

Suggested Citation

  • R. L.J. Coetzer & R. F Rossouw & N. J. Le Roux, 2012. "Efficient maximin distance designs for experiments in mixtures," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(9), pages 1939-1951, May.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:9:p:1939-1951
    DOI: 10.1080/02664763.2012.697131
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    References listed on IDEAS

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    1. J. Gower & P. Legendre, 1986. "Metric and Euclidean properties of dissimilarity coefficients," Journal of Classification, Springer;The Classification Society, vol. 3(1), pages 5-48, March.
    2. A. Narayanan, 1991. "Maximum Likelihood Estimation of the Parameters of the Dirichlet Distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 40(2), pages 365-374, June.
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