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Activity Number:
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154
- Some New Innovations in Survey Sampling and Missing Data Problems
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Type:
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Topic-Contributed
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Date/Time:
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Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Survey Research Methods Section
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Abstract #317042
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Title:
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General-Purpose Multiply Robust Estimation Procedure for Handling Missing Data
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Author(s):
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Sixia Chen* and David Haziza
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Companies:
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University of Oklahoma Health Sciences Center and University of Ottawa
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Keywords:
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Estimating equation;
Imputation;
Nonresponse adjustment;
Variance estimation
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Abstract:
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Missing data happens frequently in practice. Inverse probability weighting and imputation have been regarded as two major methods for handling missing data. However, the validity of those methods depend on the underlying model assumptions. We propose a general-purpose multiple robust estimation procedure by combining multiple nonresponse and imputation models. Our proposed method can be used to estimate parameters defined as the solution of the estimating equations. Population means, quantiles, and distribution functions are special cases of those parameters. Asymptotic results are established in the paper. We compare our proposed method with some existing methods in both simulation study and real application.
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Authors who are presenting talks have a * after their name.
