B0480
Title: Design selection for multi- and mixed-level supersaturated designs
Authors: Rakhi Singh - Binghamton University (United States) [presenting]
Abstract: The literature offers various design selection criteria and analysis techniques for supersaturated designs. The traditional optimality criteria do not work for supersaturated designs; as a result, most criteria aim to minimize some function of pairwise orthogonality between different factors. For two-level designs, the Gauss-Dantzig selector is often preferred for analysis, but it fails to capture differences in screening performance among different designs. Two recently proposed criteria utilizing large-sample properties of the Gauss-Dantzig selector by Singh and Stufken result in better screening designs. Unfortunately, the straightforward extension of these criteria to higher-level designs is not possible. For example, it is unclear if the Gauss-Dantzig selector is still an appropriate analysis method for multi- and mixed-level designs. It is first argued that group LASSO is a more appropriate method to analyze such data. Large sample properties of group LASSO is then used to propose new optimality criteria and construct novel and efficient designs that demonstrate superior screening performance.
