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Screening Procedure for Supersaturated Designs Using a Bayesian Variable Selection Method


Correspondence to: Ray-Bing Chen, Department of Statistics, National Cheng Kung University, Tainan 701, Taiwan.



A supersaturated design is a design where all effects cannot be estimated simultaneously due to an insufficient run size. An important goal in analyzing such designs is to screen active effects based on the factor sparsity assumption. In this work, a screening procedure is proposed using an efficient Bayesian variable selection approach. A modified cross-validation method is employed for parameter tuning to improve the selection results. Simulations and several real examples are used to demonstrate the performance of this screening procedure. In the real examples, our procedure identifies models similar to those of previous analysis methods. The simulation results indicate that our new procedure outperforms the other analysis methods in terms of the high true identified rate and the efficient estimation of the model size. Copyright © 2012 John Wiley & Sons, Ltd.