Abstract Details
Activity Number:
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414
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Type:
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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 #312075
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Title:
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Bayesian Sample Size Determination for Informative and Complementary Hypotheses
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Author(s):
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Kristen Tecson*+ and John W. Seaman
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Companies:
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Baylor University and Baylor University
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Keywords:
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Bayesian ;
sample size ;
informative hypotheses
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Abstract:
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Researchers often construct models using order-restricted parameters. In Bayesian hypothesis testing, incorporating inequality constraints on model parameters has led to "informative hypotheses" and associated priors. The benefits of incorporating inequality constraints directly into hypotheses instead of performing ad hoc multiple tests are well documented in the frequentist paradigm. Similar improvements are seen in operating characteristics of Bayesian hypothesis tests. Few sample size determination techniques have been explored for these problems. In this poster session, we utilize a Bayesian approach to investigate the required sample sizes necessary to distinguish between informative and complementary hypotheses.
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Authors who are presenting talks have a * after their name.
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