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Activity Number:
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504
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Type:
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Contributed
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Date/Time:
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Thursday, August 10, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #306631 |
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Title:
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A Bayesian Multiple Comparison Procedure for Order-Restricted Mixed Models
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Author(s):
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Junfeng Shang*+ and Farroll T. Wright and Joseph E. Cavanaugh
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Companies:
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Bowling Green State University and University of Missouri and The University of Iowa
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Address:
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450 Mathematical Sciences Building, Bowling Green, OH, 43403,
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Keywords:
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hierarchical model ; simple order restriction ; Gibbs sampling ; posterior probability ; hypothesis testing ; two-way ANOVA mixed model
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Abstract:
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A Bayesian hierarchical mixed model is developed for multiple comparisons under a simple-order restriction. The model makes inferences on the successive differences of the population means, for which we choose independent prior distributions that are mixtures of an exponential distribution and a discrete distribution with its entire mass at 0. We employ Markov Chain Monte Carlo (MCMC) methods to estimate parameters and to obtain estimates of the posterior probabilities that any two of the means are equal, which allow one both to determine if these two means are significantly different and to test the homogeneity of all of the means. The simulation and application results exhibit that the proposed hierarchical model can effectively unify parameter estimation, tests of hypotheses, and multiple comparisons in one setting.
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