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Abstract Details
Activity Number:
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297
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract - #300741 |
Title:
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Multiple Imputation of High-Dimensional Mixed Incomplete Data
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Author(s):
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Ren He*+ and Thomas R. Belin
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Companies:
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University of California at Los Angeles and University of California at Los Angeles
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Address:
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51-254 CHS Building, UCLA, Los Angeles, CA, 90095,
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Keywords:
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Multiple Imputation ;
Hierarchical prior ;
MCMC ;
Parameter-Extended algorithm
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Abstract:
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It is common in applied research to have large numbers of variables with mixed data types (continuous, binary, ordinal or nomial) measures on a modest number of cases. Also, even a simple imputation model can be overparameterized when the number of variables is moderately large. Finding a joint model to accommodate multivariate data with mixed data types is challenging. Here we develop a joint multiple imputation model with multivariate normal components for continuous variables and latent-normal components for categorical variables. Following the strategy of Boscardin and Weiss (2003) and using Parameter-expanded Metropolis-Hastings estimation (Boscardin,Zhang and Belin 2008), we use a hierarchical prior for the covariance matrix centered around a parametric family. This not only substantially reduces the dimension of the parameter space but also allows the data to depart from a tightly defined structured covariance matrix. The method is compared in several simulation settings to available-case analysis and a rounding method.
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