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Evaluating the performance of memory type logarithmic estimators using simple random sampling.
Shashi Bhushan1, Anoop Kumar2, Amani Alrumayh3
1Department of Statistics, University of Lucknow, Lucknow, U.P., India.
Plos One
|December 15, 2022
Summary
This study introduces memory type logarithmic estimators for time-based surveys, enhancing survey research by incorporating past and current sample data. These novel estimators improve precision in estimating population parameters.
Area of Science:
- Statistics
- Survey Methodology
Background:
- Traditional survey estimators often rely solely on current sample data.
- This limitation can affect the accuracy of population parameter estimation over time.
Purpose of the Study:
- To propose novel memory type logarithmic estimators for time-based surveys.
- To enhance estimation accuracy by integrating past and current sample information.
Main Methods:
- Development of hybrid exponentially weighted moving average estimators.
- Derivation of the mean square error expression for proposed estimators.
- Comparative analysis with existing estimators and efficiency condition derivation.
Main Results:
- Theoretical derivation of mean square error for new estimators.
- Simulation study and real data application demonstrated improved efficiency.
- Proposed estimators showed better performance compared to existing methods.
Conclusions:
- Incorporating past and current sample information significantly improves estimator efficiency.
- Memory type logarithmic estimators offer a more robust approach for time-based surveys.
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