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A new auxiliary variables-based estimator for population distribution function under stratified random sampling and
Sohail Ahmad1, Hasnain Iftikhar2,3, Moiz Qureshi4,5
1School of Mathematics and Statistics, Central South University, Changsha, 410083, China.
This study enhances population distribution function estimation accuracy by combining stratified random sampling and non-response techniques. New estimators using auxiliary variables significantly outperform existing methods.
Area of Science:
- Statistics
- Survey Methodology
Background:
- Population distribution function estimation is crucial in sample surveys.
- Existing methods using auxiliary data with stratified random sampling and non-response techniques have limitations.
Purpose of the Study:
- To improve the accuracy of population distribution function estimation.
- To maximize accuracy under combined stratified random sampling and non-response conditions.
Main Methods:
- Utilized a study variable and two auxiliary variables (mean and ranks).
- Introduced new classes of estimators for stratified random sampling with non-response.
- Conducted theoretical and numerical estimations on real-world populations.
Main Results:
- Proposed estimators demonstrated superior performance compared to existing methods.
- Simulation analysis confirmed significant improvements in estimation accuracy.
- Comparative graphs validated the effectiveness of the new estimators.
Conclusions:
- The developed estimators offer enhanced accuracy for population distribution function estimation.
- The study provides a robust framework for handling non-response and sampling in surveys.
- Findings support the practical application of these improved estimation techniques.
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