This paper develops a new approach to domain estimation and proposes a new class of ratio estimators that is more efficient than the regression estimator and not depending on any optimality condition using the principle of calibration weightings. Some well-known regression and ratio-type estimators are obtained and shown to be special members of the new class of estimators. Results of analytical study showed that the new class of estimators is superior in both efficiency and biasedness to all related existing estimators under review. The relative performances of the new class of estimators with a corresponding global estimator were evaluated through a simulation study. Analysis and evaluation are presented.

中文翻译：

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一类有效的调查抽样域均值校正率估计器

本文开发了一种新的域估计方法，并提出了一类新的比率估计器，它比回归估计器更有效，并且不使用校准加权原理依赖任何最佳条件。获得了一些众所周知的回归和比率类型估计量，它们显示为新一类估计量的特殊成员。分析研究的结果表明，新类别的估计器在效率和偏向性方面均优于所有正在审查的相关现有估计器。通过模拟研究评估了新类别估计器与相应整体估计器的相对性能。进行分析和评估。