Response modelling approach to robust parameter design methodology using supersaturated designs

In recent years, both robust parameter designs (RPDs) and supersaturated designs (SSDs) have attracted a great deal of attention. In the present article, a combination of the above two techniques is considered. More precisely, we propose a construction of an effective SSD along with an analysis method in order to deal with the significant problem of the robust parameter design methodology (RPDM). Combining iterative Sure Independence Screening (SIS) variable selection and a penalized method, namely smoothly clipped absolute deviation (SCAD), we perform an analysis of the SSDs developed in the present work. The proposed methodology is applied in different models so as to show its effectiveness in many different scenarios, assuming both first-and second-order models of a response surface design. Two illustrative examples as well as numerous numerical experiments are conducted for plenty of cases. The results imply that the proposed method is highly effective for identifying the active effects of main factors, two-factor interactions, three-factor interactions, and the pure quadratic ones, under the assumption of effect sparsity.