Ranked set sampling (RSS) has been proved to be a cost-efficient alternative to simple random sampling (SRS). However, there are situations where some measurements are censored, which may not ensure the superiority of RSS over SRS. In this paper, the performance of the maximum likelihood estimators is examined when the data are assumed to follow a Power Lindley or a Weighted Lindley distribution, and are collected according to the original RSS or one of its two variations (the median and extreme RSS). An extensive simulation study, considering uncensored and right-censored data, and perfect and imperfect ranking, is carried out based on the two mentioned distributions in order to compare the performance of the maximum likelihood estimators from RSS-based designs with the corresponding SRS estimators. Two illustrations are presented based on real data sets. The first involves the lifetimes of aluminum specimens, while the second deals with the amount of spray mixture deposited on the leaves of apple trees.

中文翻译：

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基于排序集抽样设计的最大似然估计，针对具有未经审查和未经审查数据的Lindley分布的两个扩展

事实证明，排序集抽样（RSS）是简单随机抽样（SRS）的一种经济高效的替代方案。但是，在某些情况下，某些测量值会被审查，这可能无法确保RSS优于SRS。在本文中，当假设数据遵循幂林德利或加权林德利分布，并根据原始RSS或其两个变化之一（中值和极限RSS）收集数据时，将检查最大似然估计器的性能。 。基于上述两个分布，进行了广泛的模拟研究，其中考虑了未经审查和未经审查的数据，以及完美和不完美的排名，以便将基于RSS的设计中最大似然估计器的性能与相应的SRS估计器进行比较。基于实际数据集提供了两个插图。第一个涉及铝样品的寿命，而第二个涉及沉积在苹果树叶子上的喷雾混合物的量。