Economical experimentation methods for robust design
Technometrics
A cluster analysis selection strategy for supersaturated designs
Computational Statistics & Data Analysis
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Factor screening is an important first step in many industrial experiments where a large number of factors potentially influence a response. The purpose of screening is to identify those few factors which have a substantive influence (that is, are active) and therefore, require further investigation. This paper provides a simulation tool for comparing two-stage group screening strategies where both design and noise factors may be under study. The strategies investigated are classical group screening, in which only main effects are considered at the first stage of the experiment, and an alternative strategy of screening for two-factor interactions as well as main effects.An algorithm is described which allows the user to simulate, and hence to compare, the strategies under different selections of designs and different group sizes for the stage 1 experiment, and for different probabilities of active effects. A detailed example of the use of the algorithm shows how an appropriate strategy can be chosen based on two criteria. These criteria consider the proportion of active factorial effects that are incorrectly screened out at the first-stage experiment, and the average number of observations needed for the entire experiment.