This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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177
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Type:
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Contributed
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Date/Time:
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Monday, August 2, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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Abstract - #308065 |
Title:
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Performing Hypothesis Tests with Multiply Imputed Data
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Author(s):
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Xianchao Xie*+ and Xiao-Li Meng
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Companies:
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Harvard University and Harvard University
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Address:
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Department of Statistics, Harvard University, Cambridge, MA, 02138,
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Keywords:
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Multiple Imputation ;
Hypothesis Test ;
Complete-data Testing Statistics
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Abstract:
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Multiple imputation (Rubin, 1987), a method originally designed to handle incomplete data in public-use databases, has been extensively studied and applied in various areas. Several procedures have been proposed for hypothesis testing with multiply-imputed data (Li et al., 1991; Meng and Rubin, 1992, Barnard and Rubin, 1999). When a user has access to the complete-data point estimators or complete-data log-likelihood functions, existing methods have satisfactory performance. When only the complete-data test statistics are available, there is essentially only one method (Li et al.,1991) and it is only suitable as a screen test. This talk explores possible improvements for the latter case. Several easily implementable methods are proposed for the scalar case, but generalizing them to the multi-parameter cases is a great challenge.
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Authors who are presenting talks have a * after their name.
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