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Activity Number:
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172
- Thinking Outside the Box: Innovative Methods for Estimation and Inference for Surveys
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Type:
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Invited
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Date/Time:
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Monday, August 8, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Survey Research Methods Section
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Abstract #320346
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Title:
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Multiple Bias Calibration: A New Propensity Score Weighting Framework for Handling Selection Bias in Voluntary Samples
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Author(s):
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Jae-kwang Kim*
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Companies:
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Iowa State University
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Keywords:
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Empirical likelihood;
Survey Sampling;
Calibration
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Abstract:
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Valid statistical inference is notoriously challenging when the sample is subject to selection bias. We approach this difficult problem by employing multiple candidate models for the propensity score function combined with empirical likelihood. By incorporating the multiple propensity score (PS) models into the internal bias calibration constraint in the empirical likelihood setup, the selection bias can be safely eliminated so long as the multiple candidate models contain the true PS model. The bias calibration constraint for the multiple PS model in the empirical likelihood is called the multiple bias calibration. The multiple PS models can include both ignorable and nonignorable models. Asymptotic properties are discussed and some limited simulation studies are presented to compare with the existing methods.
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Authors who are presenting talks have a * after their name.
