Abstract

The essence of this paper is to propose some efficient combined and separate classes of estimators for estimating population mean $\overline{Y}$ under stratified simple random sampling. The bias and mean square error of the proposed classes of estimators are obtained. The proposed estimators are theoretically justified over the conventional mean estimator, classical ratio and regression estimators, Kadilar and Cingi estimators, Shabbir and Gupta estimators, Singh and Vishwakarma estimators, Koyuncu and Kadilar estimators, Singh and Solanki estimator, Yadav et al. estimator, Solanki and Singh estimator and Shahzad et al. estimator. The theoretical findings are extended with a simulation study accomplished over some artificially generated symmetric and asymmetric populations.

摘要

本文的实质是提出一些有效的组合和分离类估计量来估计总体均值$\overline{是}$在分层简单随机抽样下。获得了所提出的估计量类别的偏差和均方误差。所提出的估计量在理论上比传统均值估计量、经典比率和回归估计量、Kadilar 和 Cingi 估计量、Shabbir 和 Gupta 估计量、Singh 和 Vishwakarma 估计量、Koyuncu 和 Kadilar 估计量、Singh 和 Solanki 估计量、Yadav 等人的估计量更合理。估计器，Solanki 和 Singh 估计器以及 Shahzad 等人。估算器。通过对一些人工生成的对称和不对称种群完成的模拟研究扩展了理论发现。