B0169
Title: Factor selection in screening experiments
Authors: John Stufken - George Mason University (United States) [presenting]
Rakhi Singh - UNC Greensboro (United States)
Abstract: Screening designs are used in design of experiments when, with limited resources, important factors are to be identified from a large pool of factors. Typically, a screening experiment will be followed by a second experiment to study the effect of the identified factors in more detail. As a result, the screening experiment should ideally screen out a large number of factors to make the follow-up experiment manageable, without screening out important factors. The Gauss-Dantzig Selector (GDS) is often the preferred analysis method for screening designs. While there is ample empirical evidence that fitting a main-effects model can lead to incorrect conclusions about the factors if there are interactions, including two-factor interactions in the model increases the number of model terms dramatically and challenges the GDS analysis. We discuss a new analysis method, called Gauss Dantzig Selector Aggregation over Random Models (GDS-ARM), which aggregates the effects from different iterations of the GDS analysis using different randomly selected interactions columns each time.
