Jabeen Riffat, Zaka Azam, Nagy M, Al-Mofleh Hazem, Afify Ahmed Z
Department of Statistics, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan.
Department of Statistics, Government Graduate College of Science, Wahdat Road, Lahore, Pakistan.
Sci Rep. 2024 Oct 17;14(1):24385. doi: 10.1038/s41598-024-74424-2.
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.
在调查统计中，估计和减少总体方差至关重要。这些方差可能出现在任何抽样设计中，包括分层随机抽样，其中层权重可能会增加估计量的方差。使用额外辅助信息的校准技术有助于缓解这一问题。本文在分层随机抽样的框架内研究了三种基于校准的估计量——校准方差、校准比率和校准指数比率估计量。该研究从正态分布、伽马分布和指数分布生成数据来检验这些估计量。结果表明，所提出的校准估计量能更准确地估计总体方差，并且在分层随机抽样下估计总体方差时优于现有方法，提供了更准确可靠的估计。
