Two-Level Orthogonal Screening Designs With 24, 28, 32, and 36 Runs

Eric D. Schoen, Nha Vo-Thanh and Peter Goos ()

Journal of the American Statistical Association, 2017, vol. 112, issue 519, 1354-1369

Abstract: 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.

Date: 2017
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DOI: 10.1080/01621459.2017.1279547

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