This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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111
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 2, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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ENAR
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Abstract - #308284 |
Title:
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Multiple Imputation for Missing Values Through Conditional Semiparametric Odds Ratio Models
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Author(s):
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Hua Yun Chen*+ and Hui Xie
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Companies:
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University of Illinois at Chicago and University of Illinois at Chicago
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Address:
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1603 West Taylor Street, Chicago, IL, 60612,
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Keywords:
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Acceptance-rejection sampling ;
Dirichlet process prior ;
Gibbs sampler ;
Nonparametric Bayesian inference ;
Rejection control
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Abstract:
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We propose a multiple imputation framework that uses the conditional semiparametric odds ratio models to impute the missing values. The proposed imputation framework is more general and flexible than existing imputation methods based on the normal model. It is a compatible framework in comparison to the fully conditional specification approach. In addition, imputations based on the proposed model can be straightforwardly conducted. Algorithms for carrying out the multiple imputation through the Monte Carlo Markov Chain sampling approach are proposed. A simulation study is conducted to evaluate the performance of the proposed approach. The proposed approach is then applied to imputing missing values in bone fracture data.
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