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Activity Number:
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204
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Quality and Productivity
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| Abstract - #309032 |
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Title:
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Analysis of Optimization Experiments
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Author(s):
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James Delaney*+ and Roshan Joseph
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Companies:
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Carnegie Mellon University and Georgia Institute of Technology
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Address:
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Baker Hall 132B, Pittsburgh, PA, 15213-3890,
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Keywords:
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Empirical Bayes method ; Practical significance level ; Shrinkage estimation ; Variable selection
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Abstract:
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The typical practice for analyzing industrial experiments is to identify statistically significant effects with a 5% level of significance and then to optimize the model containing only those effects. In this article, we illustrate the danger in utilizing this approach. We propose methodology using the practical significance level, which is a quantity that a practitioner can easily specify. We also propose utilizing empirical Bayes estimation which accounts for the randomness in the observations. Interestingly, the mechanics of statistical testing can be viewed as an approximation to empirical Bayes estimation, but with a significance level in the range of 15--40%. We also establish the connections of our approach with a less known but intriguing technique proposed by Taguchi. A real example and simulations are used to demonstrate the advantages of the proposed methodology.
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