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A note on multiply robust predictive mean matching imputation with complex survey data
Sixia Chen1, David Haziza2, Alexander Stubblefield3
1Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, U.S.A.
Survey Methodology
|August 21, 2023
Summary
This study introduces a new predictive mean matching method for survey data nonresponse. It uses multiple regression models for improved accuracy and robustness, outperforming traditional single-model approaches.
Area of Science:
- Statistics
- Survey Methodology
- Data Analysis
Background:
- Item nonresponse is a significant challenge in survey data collection.
- Traditional predictive mean matching relies on a single outcome regression model, which can be restrictive.
Purpose of the Study:
- To propose a novel predictive mean matching procedure using multiple outcome regression models.
- To develop a multiply robust estimator for handling item nonresponse in surveys.
Main Methods:
- The proposed method allows specification of multiple outcome regression models.
- The resulting estimator is consistent if at least one specified model is correct.
Main Results:
- Simulation studies indicate the proposed method performs well.
- The new procedure demonstrates favorable bias and efficiency compared to existing methods.
Conclusions:
- The novel predictive mean matching approach offers enhanced robustness and accuracy.
- This method provides a flexible and reliable tool for addressing item nonresponse in complex survey data.
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