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D-optimal design of split-split-plot experiments

  • Bradley Jones
  • Peter Goos

In industrial experimentation, there is growing interest in studies that span more than one processing step. Convenience often dictates restrictions in randomization in passing from one processing step to another. When the study encompasses three processing steps, this leads to split-split-plot designs. We provide an algorithm for computing D-optimal split-split-plot designs and several illustrative examples. Copyright 2009, Oxford University Press.

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File URL: http://hdl.handle.net/10.1093/biomet/asn070
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Article provided by Biometrika Trust in its journal Biometrika.

Volume (Year): 96 (2009)
Issue (Month): 1 ()
Pages: 67-82

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Handle: RePEc:oup:biomet:v:96:y:2009:i:1:p:67-82
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  1. Bradley Jones & Peter Goos, 2007. "A candidate-set-free algorithm for generating "D"-optimal split-plot designs," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(3), pages 347-364.
  2. D. R. Bingham & E. D. Schoen & R. R. Sitter, 2004. "Designing fractional factorial split-plot experiments with few whole-plot factors," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(2), pages 325-339.
  3. Eric Schoen, 1999. "Designing fractional two-level experiments with nested error structures," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(4), pages 495-508.
  4. C. J. Brien & R. A. Bailey, 2006. "Multiple randomizations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(4), pages 571-609.
  5. GOOS, Peter, . "The usefulness of optimal design for generating blocked and split-plot response surface experiments," Working Papers 2005033, University of Antwerp, Faculty of Applied Economics.
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