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Abstract Details
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Activity Number:
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74
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #302282 |
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Title:
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Model Selection and Fit in Incomplete Longitudinal Data
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Author(s):
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Arkendu S. Chatterjee*+
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Companies:
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University of Florida
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Address:
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4100 SW 20th Avenue, Gainesville, FL, 32607,
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Keywords:
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Posterior Predictive Distribution ;
MCMC ;
Bayes Factor ;
DIC ;
Mixture Model ;
Selection Model
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Abstract:
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We explore the use of posterior predictive loss for model selection for incomplete longitudinal data. We show that a straightforward extension to incomplete data introduces an extra term (in addition to the goodness of fit and penalty terms) that compromises the criterion. We select different type of Mixture models and Selection model to real data set and also used those models to simulated data sets. Our next exploration is about how good the best model fits to the data through posterior predictive assessment.
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