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

Amber Asghar, Aamir Sanaullah, Hina Khan and Muhammad Hanif
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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

Bulletin of Business and Economics (BBE), 2023, vol. 12, issue 3, 573-579

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.

Keywords: regression-cum-exponential estimator; two-phase sampling; multi-auxiliary variables; variance estimation; simulation study (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:rfh:bbejor:v:12:y:2023:i:3:p:573-579

DOI: 10.61506/01.00071

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