Abstract Details
Activity Number:
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381
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #311406
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View Presentation
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Title:
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A Bayesian Perspective on Unreplicated Factorial Experiments Using Potential Outcomes
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Author(s):
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Valeria Espinosa*+ and Donald B. Rubin and Tirthankar Dasgupta
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Companies:
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Harvard and Harvard and Harvard
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Keywords:
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posterior predictive checks ;
factorial experiments ;
potential outcomes ;
randomization tests
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Abstract:
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Factorial designs have been widely used in many scientific and industrial settings, where it is important to distinguish "active'' or real factorial effects from "inactive" or noise factorial effects used to estimate residual or "error'" terms. We propose a new approach to screen for active factorial effects from such experiments that utilizes the potential outcomes framework and is based on sequential posterior predictive model checks. One advantage of the proposed method lies in its ability to broaden the standard definition of active effects and to link their definition to the population of interest. Another important aspect of this approach is its conceptual connection to Fisherian randomization tests. Extensive simulation studies are conducted, which demonstrate the advantages of the proposed approach over existing ones.
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Authors who are presenting talks have a * after their name.
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