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Optimum second call imputation in PPS sampling.

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This study introduces a new method for handling missing data in probability proportional to size (PPS) sampling. The proposed imputation technique improves accuracy for sensitive quantitative variables, outperforming existing estimators.

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Area of Science:

  • Statistics
  • Survey Methodology
  • Data Science

Background:

  • Item non-response is a significant challenge in probability proportional to size (PPS) sampling.
  • Accurate imputation is crucial for unbiased survey estimates, especially with sensitive quantitative variables.

Purpose of the Study:

  • To propose a novel imputation procedure for item non-response in PPS sampling.
  • To leverage the covariance between study and auxiliary variables for improved imputation.
  • To evaluate the performance of the new method against existing estimators.

Main Methods:

  • Developed a new imputation procedure incorporating a randomization mechanism for non-response on the second call.
  • Utilized the known covariance between the study variable and an auxiliary variable.
  • Conducted an empirical study comparing the proposed estimator with ratio, difference, and Hansen-Hurwitz estimators.

Main Results:

  • The proposed imputation procedure demonstrated superior performance in empirical comparisons.
  • The new method showed improved accuracy for sensitive quantitative variables under PPS sampling.
  • Optimal values for 'kog' and 'nog' were identified for estimator comparison.

Conclusions:

  • The novel imputation procedure offers a more effective approach to handling item non-response in PPS surveys.
  • The method's reliance on covariance enhances its utility for sensitive data.
  • This research contributes to more reliable survey data analysis.