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Activity Number:
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105
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 3, 2009 : 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 - #304230 |
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Title:
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Bayesian Choice of Links and Computation in Binary Regression Models
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Author(s):
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Ming-Hui Chen*+ and Sungduk Kim and Lynn Kuo and Wangang Xie
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Companies:
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University of Connecticut and National Institute of Child Health and Human Development and University of Connecticut and University of Connecticut
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Address:
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Department of Statistics, Storrs, CT, 06269,
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Keywords:
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Marginal Likelihood ; Model Comparison ; t-link ; MCMC ; Posterior distribution
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Abstract:
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One of most critical issues involved in modeling binary response data is the choice of the links. We consider a class of links based on the generalized $t$-distributions. We randomly split the data into the test cohort and validation cohort. The marginal likelihood is used for guiding the choice of links in the test data and the predictive mean squared error is used to validate the choice of links in the validation data. A new Monte Carlo method is developed for computing the marginal likelihood. A real prostate cancer data set is used to illustrate the proposed methodology.
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