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Optimal versus orthogonal and equivalent‐estimation design of blocked and split‐plot experiments

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  • Peter Goos

Abstract

This article provides an overview of the recent literature on the design of blocked and split‐plot experiments with quantitative experimental variables. A detailed literature study introduces the ongoing debate between an optimal design approach to constructing blocked and split‐plot designs and approaches where the equivalence of ordinary least squares and generalized least squares estimates are envisaged. Examples where the competing design strategies lead to totally different designs are given, as well as examples in which the optimal experimental designs are orthogonally blocked or equivalent‐estimation split‐plot designs.

Suggested Citation

  • Peter Goos, 2006. "Optimal versus orthogonal and equivalent‐estimation design of blocked and split‐plot experiments," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 60(3), pages 361-378, August.
  • Handle: RePEc:bla:stanee:v:60:y:2006:i:3:p:361-378
    DOI: 10.1111/j.1467-9574.2006.00333.x
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    References listed on IDEAS

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    1. Goos, P. & Donev, A.N., 2006. "Blocking response surface designs," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1075-1088, November.
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    Cited by:

    1. Smucker, Byran J. & Castillo, Enrique del & Rosenberger, James L., 2012. "Model-robust designs for split-plot experiments," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4111-4121.
    2. Kalliopi Mylona & Harrison Macharia & Peter Goos, 2013. "Three-level equivalent-estimation split-plot designs based on subset and supplementary difference set designs," IISE Transactions, Taylor & Francis Journals, vol. 45(11), pages 1153-1165.
    3. Sambo, Francesco & Borrotti, Matteo & Mylona, Kalliopi, 2014. "A coordinate-exchange two-phase local search algorithm for the D- and I-optimal designs of split-plot experiments," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1193-1207.
    4. JONES, Bradley & GOOS, Peter, 2012. "I-optimal versus D-optimal split-plot response surface designs," Working Papers 2012002, University of Antwerp, Faculty of Business and Economics.
    5. Xiaodong Li & Xu He & Yuanzhen He & Hui Zhang & Zhong Zhang & Dennis K. J. Lin, 2017. "The Design and Analysis for the Icing Wind Tunnel Experiment of a New Deicing Coating," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1417-1429, October.
    6. Palhazi Cuervo, Daniel & Goos, Peter & Sörensen, Kenneth, 2017. "An algorithmic framework for generating optimal two-stratum experimental designs," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 224-249.
    7. Kessels, Roselinde & Goos, Peter & Vandebroek, Martina, 2008. "Optimal designs for conjoint experiments," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2369-2387, January.

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