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Generalized entropy calibration for analyzing voluntary survey data
Yonghyun Kwon1, Jae Kwang Kim2, Yumou Qiu3
1Department of Mathematics, Korea Military Academy, Seoul 01889, Republic of Korea.
This study introduces generalized entropy calibration for analyzing voluntary survey data, controlling for selection bias. The method enhances statistical efficiency and robustness in survey sampling analysis.
Area of Science:
- Survey Sampling
- Statistical Analysis
Background:
- Voluntary survey data analysis is crucial but challenging.
- Selection bias is a key concern in survey sampling.
Purpose of the Study:
- To develop a unified approach for analyzing voluntary survey data.
- To introduce generalized entropy calibration for controlling selection bias.
Main Methods:
- Generalized entropy calibration for weighting.
- Establishing the dual relationship for regression estimation.
- A two-step calibration method for smoothing weights.
Main Results:
- The proposed method controls selection bias.
- Demonstrated double robustness and local efficiency of the estimator.
- Identified implied regression models for calibration weighting.
Conclusions:
- Generalized entropy calibration offers a unified and robust approach.
- The method improves statistical efficiency in voluntary survey data analysis.
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