This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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64
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 1, 2010 : 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 - #306954 |
Title:
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Nonparametric Bayes Stochastically Ordered Latent Class Models
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Author(s):
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Hongxia Yang*+ and David Dunson
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Companies:
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Duke University and Duke University
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Address:
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, , ,
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Keywords:
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Factor analysis ;
Latent variables ;
Model-based clustering ;
Order restriction ;
Random probability measure ;
Stick-breaking
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Abstract:
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Latent class models (LCMs) are used increasingly for addressing a broad variety of problems, including sparse modeling of multivariate and longitudinal data, model-based clustering,and flexible inferences on predictor effects. However, Bayes methods relying on Markov chain Monte Carlo sampling encounter a challenging label ambiguity problem, which makes it difficult to perform inferences on class-specific quantities. In this article,we propose a new nonparametric Bayes model that allows predictors to flexibly impact the allocation to latent classes, while limiting sensitivity to parametric assumptions and label switching problems by allowing class-specific distributions to be unknown subject to a stochastic ordering constraint. The methods are validated using simulation studies and applied to the problem of ranking medical procedures in terms of the distribution of patient morbidity.
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Authors who are presenting talks have a * after their name.
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