D-optimal design of split-split-plot experiments
AbstractIn 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 provide many illustrative examples. We then apply our methods to construct D-optimal alternatives to a previously run split-split-plot design for cheese production.
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Bibliographic InfoPaper provided by University of Antwerp, Faculty of Applied Economics in its series Working Papers with number 2007017.
Length: 31 pages
Date of creation: Sep 2007
Date of revision:
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Postal: Prinsstraat 13, B-2000 Antwerpen
Web page: https://www.uantwerp.be/en/faculties/applied-economic-sciences/
More information through EDIRC
D-optimality; Exchange algorithm; Hard-to-change factors; Multi-stratum design; Split-plot design; Tailor-made design;
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- 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.
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- 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.
- 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.
- 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.
- ARNOUTS, Heidi & GOOS, Peter, 2009. "Design and analysis of industrial strip-plot experiments," Working Papers 2009007, University of Antwerp, Faculty of Applied Economics.
- Loeza-Serrano, S. & Donev, A.N., 2014. "Construction of experimental designs for estimating variance components," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1168-1177.
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