This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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302
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 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 - #307587 |
Title:
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MCMC Algorithms for Bayesian Models Involving Binary Data
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Author(s):
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Santanu Pramanik*+ and Edward Mulrow
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Companies:
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NORC and NORC
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Address:
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, , ,
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Keywords:
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Mixture model ;
logit-normal model ;
Metropolis Hastings ;
Holmes and Held ;
Gibbs sampler ;
Bayesian computation
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Abstract:
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In the context of accounting reconciliation data, where estimation of the total error amount of an accounting system and numerous subsystems is of interest, a hierarchical Bayesian mixture model can be used. In this paper, we compare and contrast two different MCMC methods for a logit-normal model, one component of the mixture model. The Metropolis Hastings algorithm requires tuning of the covariance structure for the candidate distribution to achieve satisfactory performance. In some multi-parameter situations, as is ours, this can be a daunting task. The Holmes and Held algorithm uses a Gibbs sampler, after introducing latent variables, to avoid an accept-reject step and hence any tuning, but it may be a slow-running algorithm. We will illustrate the pros and cons of the two algorithms and look at a hybrid approach that might reduce the computation time considerably.
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Authors who are presenting talks have a * after their name.
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