In industrial experimentation, experimental designs are frequently constructed to estimate all main effects and a fewprespecified interactions. The robust-product-design literatureis replete with such examples. A major limitation of this approach is the requirement that the experimenter know which interactions are likely to be active in advance. In this article, we develop a class of balanced designs that can be used for estimation of main effects and any combination of up to CJin teractions, where g is specified by the user. We view this as an issue of model-robust design: We construct designs that are highly efficient for all models involving main effects and g (or fewer) interactions. We compare the performances of these designs with the standard alternatives from the class of maximum-resolution fractional factorial designs for several criteria. The comparison reveals that the new designs are surprisingly robust to model misspecification, something that is generally not true for maximum-resolution fractional factorial designs. This robustness comes at a price: The new designs are frequently not orthogonal. We demonstrate, however, that the loss of orthogonality is, in general, quite small.
Bibliographical noteFunding Information:
This research was supported by the Research and Teaching Supplements system in Carlson School of Management at the University of Minnesota. We thank the editor, the associate editor, and the referees for insightful comments that led to substantive improvements to our article. We also thank Lexin Li for JAVA programming assistance. This work was partially supported by the IT Statistical Consulting Department, 3M.
- Estimation canacitv
- Exchange algorithm
- Information capacity
- Model robust design
- Optimal design