A new modified estimator of population variance in calibrated survey sampling.

Autor: Jabeen R; Department of Statistics, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan., Zaka A; Department of Statistics, Government Graduate College of Science, Wahdat Road, Lahore, Pakistan., Nagy M; Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia., Al-Mofleh H; Department of Mathematical Sciences, Ball State University, Muncie, IN, 47306, USA., Afify AZ; Department of Statistics, Mathematics, and Insurance, Benha University, Benha, 13511, Egypt. ahmed.afify@fcom.bu.edu.eg.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Oct 17; Vol. 14 (1), pp. 24385. Date of Electronic Publication: 2024 Oct 17.
DOI: 10.1038/s41598-024-74424-2
Abstrakt: In survey statistics, estimating and reducing population variation is crucial. These variations can occur in any sampling design, including stratified random sampling, where stratum weights may increase the variance of estimators. Calibration techniques, which use additional auxiliary information, can help mitigate this issue. This paper examines three calibration-based estimators-calibration variance, calibration ratio, and calibration exponential ratio estimators-within the framework of stratified random sampling. The study generates data from normal, gamma, and exponential distributions to test these estimators. Results demonstrate that the proposed calibration estimators offer more accurate estimates of population variance and outperform existing methods in estimating population variance under stratified random sampling, providing more accurate and reliable estimates.
(© 2024. The Author(s).)
Databáze: MEDLINE
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