Analysis of two-level supersaturated designs using the Modified Box-Meyer method

Abstract

In a two-level supersaturated design (L2SSD), there are more runs (columns) than factors (rows). This approach is commonly used in screening experiments, where the goal is low-cost identification of active factors (i.e., have major influence on the response). Several previous studies have considered methods for selecting the active factors of L2SSDs under the assumption of effect sparsity, with the Box–Meyer method (BMM) performing the best. However, to overcome various drawbacks of BMM, a modified BMM (MBMM) is proposed by Samset and Tyssedal(1998) to analyze the data of fractionated designs. However, MBMM is not used for L2SSDs so far. Therefore we propose here a modified BMM (MBMM) for analyzing L2SSDs. Although both methods can select active factors, MBMM selects fewer inactive factors compared to BMM. The present simulation results confirm that MBMM is an excellent analytical tool for L2SSDs.