Journal
TECHNOMETRICS
Volume 52, Issue 2, Pages 231-242Publisher
AMER STATISTICAL ASSOC
DOI: 10.1198/TECH.2010.08093
Keywords
Aberration; Design optimality criterion; Generalized minimum aberration; Model robust; Projection efficiency; Supersaturated design
Categories
Funding
- University of London
- U.K. Engineering and Physical Sciences Research Council
- Isaac Newton Institute for Mathematical Sciences
- National Science Council of Taiwan
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Motivated by two industrial experiments in which rather extreme prior knowledge was used to choose the design, we show that the Q(B) criterion, which aims to improve the estimation in as many models as possible by incorporating experimenters prior knowledge along with an approximation to the As criterion, is more general and has a better statistical interpretation than many standard criteria. The generalization and application of the criterion to different types of designs are presented. The relationships between Q(B) and other criteria for different situations are explored. It is shown that the E(s(2)) criterion is a special case of Q(B) and several aberration-type criteria are limiting cases of our criterion, so that Q(B) provides a bridge between alphabetic optimality and aberration. The two case studies illustrate the potential benefits of the Q(B) criterion. R programs or calculating Q(B) are available online as supplemental materials.
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