Cost effective sampling design is a problem of major concern in some experiments especially when the measurement of the characteristic of interest is costly or painful or time consuming. In the current paper, a modification of ranked set sampling (RSS) called moving extremes RSS (MERSS) is considered for the estimation of the location parameter for location family. A maximum likelihood estimator (MLE) of the location parameter for this family is studied and its properties are obtained. We prove that the MLE is an equivariant estimator under location transformation. In order to give more insight into the performance of MERSS with respect to (w.r.t.) simple random sampling (SRS), the asymptotic efficiency of the MLE using MERSS w.r.t. that using SRS is computed for some usual location distributions. The relative results show that the MLE using MERSS can be real competitors to the MLE using SRS.

中文翻译：

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移动极值排序集抽样设计下位置参数的最大似然估计

成本有效的抽样设计是一些实验中主要关注的问题，尤其是当感兴趣特性的测量成本高昂、痛苦或耗时时。在当前的论文中，考虑了一种称为移动极值 RSS (MERSS) 的排序集采样 (RSS) 的修改，用于估计位置族的位置参数。研究了这个族的位置参数的最大似然估计（MLE）并获得了它的性质。我们证明了 MLE 是位置变换下的等变估计量。为了更深入地了解 MERSS 在 (wrt) 简单随机采样 (SRS) 方面的性能，使用 MERSS 的 MLE 的渐近效率与使用 SRS 的 MLE 是针对一些常用位置分布计算的。