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Comparative Analysis of Variance Estimation Methods in Two-Phase Sampling: A Focus on Regression-cum-Exponential Estimators with Multiple Auxiliaries

Author

Listed:
  • Amber Asghar

    (Virtual University of Pakistan Lahore, Pakistan)

  • Aamir Sanaullah

    (COMSATS University Islamabad Lahore Campus, Pakistan)

  • Hina Khan

    (Government College University, Lahore, Pakistan)

  • Muhammad Hanif

    (NCBA&E, Lahore, Pakistan)

Abstract

In this study, we introduce a regression-cum-exponential estimator designed for estimating population variance. Specifically, we focus on the estimation of unknown population variance in a two-phase sampling setup, considering the use of multiple auxiliary variables. We derive and discuss various cases pertaining to this estimation framework. Additionally, we compare the asymptotic properties of existing approaches with those of our proposed estimator. This allows us to assess the performance and efficiency of the different methods. Finally, we conduct a simulation study to evaluate the performance of our proposed estimator in finite samples, specifically utilizing multi-auxiliary variables. This empirical analysis provides insights into the practical effectiveness of the estimators.

Suggested Citation

  • Amber Asghar & Aamir Sanaullah & Hina Khan & Muhammad Hanif, 2023. "Comparative Analysis of Variance Estimation Methods in Two-Phase Sampling: A Focus on Regression-cum-Exponential Estimators with Multiple Auxiliaries," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 12(3), pages 573-579.
    • Handle: RePEc:rfh:bbejor:v:12:y:2023:i:3:p:573-579
    DOI: https://doi.org/10.61506/01.00071
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