Augmenting supersaturated designs with Bayesian D-optimality

Alex J. Gutman, Edward D. White, Dennis K.J. Lin and Raymond R. Hill

Computational Statistics & Data Analysis, 2014, vol. 71, issue C, 1147-1158

Abstract: A methodology is developed to add runs to existing supersaturated designs. The technique uses information from the analysis of the initial experiment to choose the best possible follow-up runs. After analysis of the initial data, factors are classified into one of three groups: primary, secondary, and potential. Runs are added to maximize a Bayesian D-optimality criterion to increase the information gained about those factors. Simulation results show the method can outperform existing supersaturated design augmentation strategies that add runs without analyzing the initial response variables.

Keywords: Adding runs; Augmentation; Computer-generated designs; Experimental design; Screening designs; Supersaturated designs (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:71:y:2014:i:c:p:1147-1158

DOI: 10.1016/j.csda.2013.09.009

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