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Activity Number:
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345
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 6, 2008 : 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 - #300614 |
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Title:
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Bayesian Model Checking for Multivariate Data
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Author(s):
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Catherine M. Crespi*+ and W. John Boscardin
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Companies:
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University of California, Los Angeles and University of California, Los Angeles
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Address:
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School of Public Health and Jonsson Comprehensive Cancer Center, Los Angeles, CA, 90095-1772,
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Keywords:
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Bayesian analysis ; Dissimilarity measures ; Multivariate data ; Posterior predictive model checking
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Abstract:
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Bayesian models are increasingly used to analyze complex multivariate data. However, methods for checking such models have not been well-developed. We present a method of evaluating the fit of Bayesian models for multivariate data based on posterior predictive model checking (PPMC), a technique in which observed data are compared to replicated data generated from model predictions. We introduce the use of dissimilarity measures for PPMC for multivariate data. This method has the advantage of checking the fit of the model to the complete data vectors or vector summaries with reduced dimension, providing a overall picture of model fit. An application with longitudinal binary data from a clinical trial illustrates the methods.
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