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Enhanced log ratio calibration methods for stratified variance estimation in survey sampling.

Azam Zaka1, Fatimah M Alghamdi2, Sara M A Alsheikh3

  • 1Department of Statistics, Government Graduate College of Science, Wahdat Road, Lahore, Pakistan.

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|December 1, 2025
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This study introduces new calibrated log-ratio estimators for population variance in stratified random sampling. These improved methods reduce mean squared error, enhancing the accuracy of survey data analysis.

Keywords:
CalibrationLog ratio estimatorStratificationStratified random samplingVariance estimator

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Area of Science:

  • Statistics
  • Survey Methodology

Background:

  • Stratified random sampling is crucial for accurate population estimates by dividing data into subgroups.
  • Existing methods for estimating population variance may not fully leverage the benefits of stratification.

Purpose of the Study:

  • To develop and evaluate novel calibrated log-ratio-type estimators for population variance.
  • To compare the performance of these new estimators against existing methods using mean squared error.

Main Methods:

  • Proposing a set of improved calibrated log-ratio-type estimators.
  • Conducting a simulation study to assess estimator efficiency.
  • Applying the estimators to a real-life dataset for validation.

Main Results:

  • The proposed calibrated log-ratio variance estimators demonstrated superior performance.
  • The new estimators achieved a lower mean squared error compared to existing techniques.
  • Simulation and real-life application results confirmed the enhanced efficiency.

Conclusions:

  • The developed calibrated log-ratio estimators offer significant improvements for variance estimation in stratified sampling.
  • These estimators enhance the precision and accuracy of survey data analysis.
  • The findings support the adoption of these advanced methods in statistical practice.