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This study introduces novel log-type imputation techniques for handling missing survey data. These methods improve population mean estimation, showing promising results in simulations and real-world data analysis.

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Area of Science:

  • Statistics
  • Survey Methodology
  • Data Science

Background:

  • Missing data is a common challenge in surveys, potentially biasing results.
  • Accurate estimation of population parameters is crucial for reliable survey findings.
  • Existing imputation methods may have limitations in certain data distributions.

Purpose of the Study:

  • To propose three new classes of log-type imputation techniques.
  • To derive corresponding point estimators for the population mean.
  • To evaluate the performance of these new techniques.

Main Methods:

  • Development of novel log-type imputation methods.
  • Derivation of point estimators for population mean.
  • Bias and Mean Square Error analysis.
  • Extensive simulation studies with various distributions (Normal, Poisson, Gamma) and a real dataset.

Main Results:

  • The proposed imputation techniques and estimators demonstrate competitive or superior performance compared to existing methods.
  • Performance evaluation indicates effectiveness across different data distributions.
  • Analysis of bias and Mean Square Error provides insights into estimator properties.

Conclusions:

  • The new log-type imputation techniques offer a valuable addition to the toolkit for handling missing survey data.
  • The proposed estimators are effective for population mean estimation, particularly in scenarios with missing values.
  • The findings support the practical application of these methods in real-world survey analysis.