Analysis Methods for Supersaturated Design: Some Comparisons

Abstract

Supersaturated designs are very cost-effective with respect to the number of runs and as such are highly desirable in many preliminary studies in industrial experimentation. Variable selection plays an important role in analyzing data from the supersaturated designs. Traditional approaches, such as the best subset variable selection and stepwise regression, may not be appropriate in this sit-uation. In this paper, we introduce a variable selection procedure to screen active effects in the SSDs via nonconvex penalized least squares approach. Empirical comparison with Bayesian variable se-lection approaches is conducted. Our simulation shows that the non-convex penalized least squares method compares very favorably with the Bayesian variable selection approach proposed in Beattie, Fong and Lin (2001).