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Screening Procedure for Supersaturated Designs Using a Bayesian Variable Selection Method
Author(s) -
Chen RayBing,
Weng JianZhong,
Chu ChiHsiang
Publication year - 2013
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1299
Subject(s) - selection (genetic algorithm) , computer science , bayesian probability , variable (mathematics) , model selection , design of experiments , machine learning , statistics , artificial intelligence , mathematics , mathematical analysis
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.