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Title
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Two-level orthogonal screening designs with 24, 28, 32, and 36 runs
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Author
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Abstract
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| The potential of two-level orthogonal designs to fit models with main effects and two-factor interaction effects is commonly assessed through the correlation between contrast vectors involving these effects. We study the complete catalog of nonisomorphic orthogonal two-level 24-run designs involving 3-23 factors and we identify the best few designs in terms of these correlations. By modifying an existing enumeration algorithm, we identify the best few 28-run designs involving 3-14 factors and the best few 36-run designs in 3-18 factors as well. Based on a complete catalog of 7570 designs with 28 runs and 27 factors, we also seek good 28-run designs with more than 14 factors. Finally, starting from a unique 31-factor design in 32 runs that minimizes the maximum correlation among the contrast vectors for main effects and two-factor interactions, we obtain 32-run designs that have low values for this correlation. To demonstrate the added value of our work, we provide a detailed comparison of our designs to the alternatives available in the literature. Supplementary materials for this article are available online. |
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Language
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English
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Source (journal)
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Journal of the American Statistical Association. - Washington, D.C.
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Publication
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Alexandria
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Amer statistical assoc
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2017
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ISSN
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0162-1459
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DOI
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10.1080/01621459.2016.1279547
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Volume/pages
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112
:519
(2017)
, p. 1354-1369
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ISI
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000416611500041
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Full text (Publisher's DOI)
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