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Abstract Details
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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 - #300875 |
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Title:
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Maximum-Likelihood-Based Multiple Imputation
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Author(s):
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Tejas A. Desai*+
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Companies:
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Adani Institute of Infrastructure Management
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Address:
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25 Saurashtra Society, Ahmedabad, International, 380007, India
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Keywords:
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Imputation ;
Fisher Information ;
Missing data ;
maximum likelihood ;
Frequentist Analysis ;
General Location Model
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Abstract:
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Donald Rubin pioneered the use of Bayesian multiple imputation for analyzing a wide variety of incomplete data. Specifically, the general location model was proposed and used to impute entire data sets. Desai and Sen (2006, 2008) developed a frequentist method for analyzing randomly incomplete data without imputation by characterizing the underlying Fisher information appropriately. However, there are situations where imputation is necessary. In this paper, we propose and demonstrate the use of maximum-likelihood-based multiple imputation. After briefly outlining the theory, we present simulations and an example.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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