Abstract

Screening is the process of using designed experiments and statistical analyses to search through a large number of potentially influential factors in order to discover the few factors that have a substantial effect on a measured response (i.e. that are “active”). In this setting, conventional fractional factorial experiments typically require too many observations to be economically viable. To overcome this problem in practice, interactions are often dropped from consideration and assumed to be negligible, sometimes without substantive justification. Such loss of information can be a serious problem in industrial experimentation because exploitation of interactions is a key tool for product improvement. This talk describes an assessment and comparison of two screening strategies for interactions, namely supersaturated designs and group screening, together with a variety of data analysis methods, based on shrinkage regression and Bayesian methods. Recommendation s on the use of the screening strategies are provided through simulation studies.