Improving Population Variance Estimation with Randomized Response Models
Author Information
Author(s): Shabbir Javid, Movaheedi Zabihullah
Hypothesis
Can a generalized randomized response model enhance the estimation of finite population variance?
Conclusion
The proposed estimator outperforms previous estimators in estimating finite population variance using two auxiliary variables.
Supporting Evidence
- The proposed estimator showed reduced mean squared error compared to previous estimators.
- Simulation studies confirmed the efficiency of the new estimators under various scenarios.
- The study adjusted bias and MSE expressions of existing models to improve accuracy.
Takeaway
This study shows a new way to estimate how much a population varies, especially when asking sensitive questions, and it works better than older methods.
Methodology
The study involved adjusting bias and mean squared error expressions of existing estimators and conducting a simulation study to validate the proposed estimators.
Potential Biases
Potential biases in responses to sensitive questions may still affect the estimators despite using randomized response models.
Limitations
The study is limited to two auxiliary variables and may not generalize to more complex scenarios without further research.
Digital Object Identifier (DOI)
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