Analysis Methods for Supersaturated Design: Some Comparisons

10.6339/JDS.2003.01(3).134

Runze Li；Dennis K. J. Lin

Bayesian variable selection ； penalized least squares ； SCAD

Journal of Data Science

1卷3期（2003 / 07 / 01）

249 - 260

英文

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 situation. 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 selection approaches is conducted. Our simulation shows that the nonconvex penalized least squares method compares very favorably with the Bayesian variable selection approach proposed in Beattie, Fong and Lin (2001).