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Generalized entropy calibration for analyzing voluntary survey data.

Yonghyun Kwon1, Jae Kwang Kim2, Yumou Qiu3

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Summary
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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.

Keywords:
calibration generating functionregression estimationtwo-step calibrationweighting

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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.