主题：Optimal Foldovers of Orthogonal and Non-Orthogonal Designs
主办单位：统计研究中心 统计学院 科研处
(Professor in Supply Chain & Operation Dept., Carlson School of Management, U. of Minnesota)
- 冠名讲席教授(Chair Professor, Eric Jing Professor for Business Teaching and Research)
A commonly used follow-up experiment strategy involves the use of a foldover design by reversing the signs of one or more columns. In the first part of the talk we give a review of recent progress in obtaining optimal foldovers of orthogonal designs. In the second part, we develop a fast algorithm for constructing efficient two-level foldover designs that are not orthogonal. Recent work in two-level screening experiments has demonstrated the advantages of using small foldover designs, even when such designs are not orthogonal for the estimation of main effects (MEs). We provide further support for this argument. We show that these designs have equal or greater efficiency for estimating the ME model versus competitive designs in the literature and that our algorithmic approach allows the fast construction of designs with many more factors and/or runs. Our compromise algorithm allows the practitioner to choose among many designs making a trade-off between efficiency of the main effect estimates and correlation of the two-factor interactions (2FIs).