Outperforming Completely Randomized Designs
Open accessAbstract
Split-plot designs have become increasingly popular in industrial experimentation because some of the factors under investigation are often hard-to-change. It is well-known that the resulting compound symmetric error structure not only affects estimation and inference procedures but also the efficiency of the experimental designs used. In this paper, we compute D-optimal first and second order split-plot designs and show that these designs, in many cases, outperform completely randomized designs in terms of D- and G-efficiency. This suggests that split-plot designs should be considered as an alternative to completely randomized designs even if running a completely randomized design is affordable.
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Similar research was done by Goos and Vandebroek (2004), who presented an algorithm for determining the optimal split-plot structure of an experimental design.
“Similar research was done by Goos and Vandebroek (2004), who presented an algorithm for determining the optimal split-plot structure of an experimental design.”
From D-optimal design of split-split-plot experiments (2009) · cites this paper as Goos and Vandebroek (2004)
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Research works printed but not linked: 8
- Anbari, F. and Lucas, J. (1994). Super-efficient designs: How to run your experiment for higher efficiency and lower cost, ASQC Technical Conference Transactions, pp. 852- 863.
- Atkinson, A. and Donev, A. (1992). Optimum Experimental Designs, Oxford U.K.: Clar- endon Press.
- Davison, J. (1995). Response Surface Designs and Analysis for Bi-Randomization Error Structures, Ph.d. thesis, Virginia Polytechnic Institute and State University.
- Fedorov, V. (1972). Theory of Optimal Experiments, New York: Academic Press.
- Ganju, J. and Lucas, J. (1999). Detecting randomization restrictions caused by factors, Journal of Statistical Planning and Inference 81: 129-140.
- Kempthorne, O. (1952). The Design and Analysis of Experiments, New York: Wiley.
- Letsinger, J., Myers, R. and Lentner, M. (1996). Response surface methods for bi- randomization structures, Journal of Quality Technology 28: 381-397.
- Nelson, L. (1985). What do low F ratios tell you?, Journal of Quality Technology 17: 237- 238.
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D-optimal design of split-split-plot experiments
Jones, B., & Goos, P. (2009) · Biometrika · doi:10.1093/biomet/asn070 · Cited by 47 · see citing works
Self-citation
- Authors
- Goos, P., & Vandebroek, M.
- Affiliations
- KU Leuven (BE)
- Venue
- Journal of Quality Technology
- Publisher
- Informa UK Limited
- Published
- 2004-01
- Volume
- 36
- Issue
- 1
- Pages
- 12-26
- ISSN
- 0022-4065 (ISSN-L)
- 2575-6230
- OpenAlex
- W1525349988
- Cited by
- 86
- References
- 0
- Citations by year
- 2026: 2 · 2024: 6 · 2023: 2 · 2021: 2 · 2019: 3 · 2018: 4 · 2017: 5 · 2016: 1 · 2015: 4 · 2014: 1 · 2013: 3 · 2012: 8
- FWCI
- 12.67
- Type
- journal article
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Licence
- other-oa
- Locations
- Journal of Quality Technology (publishedVersion)
- Lirias (submittedVersion, other-oa)
- Funders
- Vlaamse regering
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
- Keywords
- split-plot design, completely randomized design, D-optimal design, D-efficiency, industrial experiments
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