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Abstract：
The existence of measurement errors cannot be avoided in practice. It is a prominent fact that the existence of measurement errors diminishes conventional properties of the estimators. A modified correlated measurement errors model has been proposed. Shalabh and Tsai (Commun Stat Simul Comput 46(7):5566-5593. 10.1080/03610918.2016.1165845, 2017) correlated measurement errors model is a particular member of the suggested modified model. In this article, we have tackled the estimation of population mean utilizing auxiliary information under modified correlated measurement errors model. We have developed ratio and product estimators and studied their properties in case of simple random sampling without replacement (SRSWOR) up to first order of approximation. It has been illustrated that suggested ratio and product estimators are more efficient than the conventional unbiased estimator as well as Shalabh and Tsai (Commun Stat Simul Comput 46(7):5566-5593. 10.1080/03610918.2016.1165845, 2017) ratio and product estimators under very realistic situations. An empirical study has also been performed to demonstrate the merits of the recommended estimators over other estimators.
摘要：
测量误差的存在在实际中是无法避免的。一个突出的事实是，测量误差的存在会降低估计器的常规特性。提出了一种改进的相关测量误差模型。ShalabhandTsai（CommunStatSimulComput46（7）：5566-5593。10.1080/03610918.2016.1165845，2017）相关测量误差模型是建议的修改模型的特定成员。在这篇文章中,我们在修正的相关测量误差模型下，利用辅助信息解决了总体均值的估计问题。我们已经开发了比率和乘积估计器，并研究了它们在简单随机抽样而无需替换（SRSWOR）直至一阶近似的情况下的性质。已经证明，建议的比率和乘积估计器比传统的无偏估计器以及Shalabh和Tsai（CommunStatSimulComput46（7）：5566-5593。10.1080/03610918.2016.1165845，2017）在非常现实的情况下的比率和产品估计器。还进行了一项实证研究，以证明推荐的估计量优于其他估计量。
