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Ratio-cum-product Type Estimators for Rare and Hidden Clustered Population.

Rajesh Singh1, Rohan Mishra1

  • 1Institute of Science, Department of Statistics, Banaras Hindu University, Varanasi, India.

Sankhya. Series B (2008)
|December 19, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces novel ratio-cum-product estimators using two auxiliary variables in adaptive cluster sampling (ACS). These new estimators demonstrate improved precision and efficiency, particularly for rare and clustered populations like COVID-19 spread.

Keywords:
COVID-19adaptive cluster samplingcombining estimators.product estimatorratio estimatorregression estimator

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Area of Science:

  • Statistics
  • Survey Methodology
  • Epidemiology

Background:

  • Estimator precision is crucial, especially for rare and clustered populations.
  • Adaptive cluster sampling (ACS) is effective for such populations.
  • Auxiliary variables can enhance estimator precision.

Purpose of the Study:

  • To propose four novel ratio-cum-product type estimators.
  • To utilize two auxiliary variables within an ACS design.
  • To improve estimation precision for rare and clustered populations.

Main Methods:

  • Developed four ratio-cum-product estimators under ACS.
  • Derived Mean Square Error (MSE) expressions to the first order approximation.
  • Assessed efficiency against existing estimators.

Main Results:

  • Proposed estimators showed improved precision and efficiency.
  • Performance was evaluated on four diverse populations, including COVID-19 data.
  • The new estimators outperformed existing ones across all tested populations.

Conclusions:

  • The proposed ratio-cum-product estimators are highly effective.
  • They offer significant precision gains for rare and clustered populations.
  • Demonstrated wide applicability and superior performance in real-world scenarios.