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

• 成果类型: Review
• 署名作者: Schoen, Eric D.; Nha Vo-Thanh; Goos, Peter
• 署名单位: University of Antwerp; Netherlands Organization Applied Science Research; KU Leuven; KU Leuven
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2017.1279547
• 发表日期: 2017
• 页码: 1354-1369
• 关键词: fractional factorial-designs; hadamard-matrices; nonregular designs; CLASSIFICATION; CONSTRUCTION; plackett; catalogs; criteria; burman

摘要

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.