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QICID: 30506
Title: Bayesian Optimal Single Arrays for Robust Parameter Design
Copyright: ASQ; American Statistical Association
Author: Kang,Lulu; Joseph, V. Roshan
Organization: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Subject: Design of experiments (DOE); Algorithm; Noise; Quality improvement (QI); Variation; Parameter design; Bayesian methods;
Series: Technometrics, Vol. 51, No. 3, August 2009, pp. 250–261
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Abstract: [This abstract is based on the authors' abstract.]
Control-by-noise interactions in robust parameter designs can be estimated by using a cross array, but the total run size of such arrays can be too large to be practical. To reduce the run size, it has been recommended that single arrays selected by using a modified effect hierarchy be used. This study proposes a Bayesian approach to develop single arrays that incorporate control-by-noise interactions without altering the effect hierarchy. A modified exchange algorithm is provided for finding the optimal single arrays, and it is shown how to design experiments with internal noise factors. Several examples illustrate the proposed approach.
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