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Activity Number:
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210
- Contributed Poster Presentations: Survey Research Methods Section
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Type:
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Contributed
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Date/Time:
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Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Survey Research Methods Section
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Abstract #313977
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Title:
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Robust Estimation of Heckman Correction in Complex Sampling Design
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Author(s):
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Michael Machiorlatti* and Sixia Chen
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Companies:
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and University of Oklahoma Health Sciences Center
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Keywords:
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Complex Sampling Design;
Heckman model;
Robust Estimation;
Sample Selection;
Selection Bias
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Abstract:
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The Heckman selection model proposed by Heckman (1979) has been used extensively in economics and social sciences to correct selection bias. It is closely related to nonignorable nonresponse problems. Prior research has shown that the Heckman model is sensitive to outlying values. Zhelonkin et al. (2016) explored robust techniques to account for these sensitivities. In this study we study extend robust techniques for the Heckman model to a complex survey setting by incorporating sampling design features. In addition, we propose efficient weight smoothing approaches to further improve efficiency of the estimates under informative sampling. We compare the performance of the classical and robust methods in a complex survey design in a Monte Carlo simulation study. Finally, we illustrate the use of our methodology in a real data application using (2015-16) National Health and Nutrition Examination Survey (NHANES).
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Authors who are presenting talks have a * after their name.
