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Biometrika 2009 96(1):67-82; doi:10.1093/biomet/asn070
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© 2009 Biometrika Trust

Articles

D-optimal design of split-split-plot experiments

Bradley Jones

SAS Institute Inc., SAS Campus Drive, Cary, North Carolina 27513, U.S.A. bradley.jones{at}jmp.com

Peter Goos

Faculty of Applied Economics, Universiteit Antwerpen, Prinsstraat 13, 2000 Antwerpen, Belgium peter.goos{at}ua.ac.be

Received for publication 1 May 2008. Revision received 1 October 2008.

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.

Key Words: Coordinate-exchange algorithm • D-optimality • Hard-to-change factor • Multi-stratum design • Split-plot design • Split-split-plot design



References

    Bingham D. R., Schoen E. D., Sitter R. R. Designing fractional factorial split-plot experiments with few whole-plot factors. Appl. Statist. (2004) 53:325–39. Corrigendum, 54, 955–8.

    Brien C. J., Bailey R. A. Multiple randomizations. J. R. Statist. Soc. B (2006) 68:571–609.[CrossRef]

    Edmondson R. N. Agricultural response surface experiments based on four-level factorial designs. Biometrics (1991) 47:1435–48.[CrossRef][Web of Science]

    Goos P. The Optimal Design of Blocked and Split-Plot Experiments (2002) New York: Springer.

    Goos P. The usefulness of optimal design for generating blocked and split-plot response surface experiments. Statist. Neer. (2006) 60:361–78.[CrossRef]

    Goos P., Donev A. N. Tailor-made split-plot designs with mixture and process variables. J. Qual. Technol. (2007) 39:326–39.

    Goos P., Vandebroek M. Optimal split-plot designs. J. Qual. Technol. (2001) 33:436–50.

    Goos P., Vandebroek M. D-optimal split-plot designs with given numbers and sizes of whole plots. Technometrics (2003) 45:235–45.[CrossRef][Web of Science]

    Goos P., Vandebroek M. Outperforming completely randomized designs. J. Qual. Technol. (2004) 36:12–26.

    Harville D. A. Matrix Algebra from a Statistician's Perspective (1997) New York: Springer.

    Harville D. A., Jeske D. R. Mean squared error of estimation or prediction under a general linear model. J. Am. Statist. Assoc. (1992) 87:724–31.[CrossRef][Web of Science]

    Jones B., Goos P. A candidate-set-free algorithm for generating D-optimal split-plot designs. Appl. Statist. (2007) 56:347–64.

    Kackar R. N., Harville D. A. Approximations for standard errors of estimators of fixed and random effects in mixed linear models. J. Am. Statist. Assoc. (1984) 79:853–62.[CrossRef][Web of Science]

    Kenward M. G., Roger J. H. Small sample inference for fixed effects from restricted maximum likelihood. Biometrics (1997) 53:983–97.[CrossRef][Web of Science][Medline]

    Lenth R. V. Quick and easy analysis of unreplicated factorials. Technometrics (1989) 31:469–73.[CrossRef][Web of Science]

    Loeppky J., Sitter R. R. Analyzing unreplicated blocked fractional factorial and fractional factorial split-plot designs. J. Qual. Technol. (2002) 34:229–43.

    Loughin T. M., Noble W. A permutation test for effects in an unreplicated factorial design. Technometrics (1997) 39:180–90.[CrossRef][Web of Science]

    Mee R., Bates R. L. Split-lot designs: experiments for multistage batch processes. Technometrics (1998) 40:127–40.[CrossRef][Web of Science]

    Meyer R. K., Nachtsheim C. J. The coordinate-exchange algorithm for constructing exact optimal experimental designs. Technometrics (1995) 37:60–9.[CrossRef][Web of Science]

    Schoen E. D. Designing fractional two-level experiments with nested error structures. J. Appl. Statist. (1999) 26:495–508.[CrossRef]

    Sun D. X., Wu C. F. J. Statistical properties of Hadamard matrices of order 16. In: Quality Through Engineering Design—Kuo W., ed. (1993) New York: Elsevier. 169–79.

    Trinca L. A., Gilmour S. G. Multi-stratum response surface designs. Technometrics (2001) 43:25–33.[CrossRef][Web of Science]

    Webb D., Lucas J. M., Borkowski J. J. Factorial experiments when factor levels are not necessarily reset. J. Qual. Technol. (2004) 36:1–11.


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This Article
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