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Activity Number: 414
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #312075
Title: Bayesian Sample Size Determination for Informative and Complementary Hypotheses
Author(s): Kristen Tecson*+ and John W. Seaman
Companies: Baylor University and Baylor University
Keywords: Bayesian ; sample size ; informative hypotheses
Abstract:

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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