A comparison of design and model selection methods for supersaturated experiments

Various design and model selection methods are available for supersaturated designs having more factors than runs but little research is available on their comparison and evaluation. In this paper, simulated experiments are used to evaluate the use of E(s2)-optimal and Bayesian D-optimal designs, and to compare three analysis strategies representing regression, shrinkage and a novel model-averaging procedure. Suggestions are made for choosing the values of the tuning constants for each approach. Findings include that (i) the preferred analysis is via shrinkage; (ii) designs with similar numbers of runs and factors can be e_ective for a considerable number of active e_ects of only moderate size; and (iii) unbalanced designs can perform well. Some comments are made on the performance of the design and analysis methods when e_ect sparsity does not hold. 

• Type: Journal
• Year: 2010
• Where: CSDA
• Authors: Christopher J. Marley, David C. Woods
• Keywords: Bayesian D-optimal Designs, CSDA 2010, Design, Model Selection Methods