Abstract

Ranked Set Sampling (RSS), introduced by McIntyre, and other related methods, such as Partially Rank-Ordered Set Sampling (PROSS), have shown that inclusion of a ranking mechanism produces estimators with lower variance than their simple random sample (SRS)-based counterparts. Like RSS, PROSS takes only one measurement from each partially ranked-ordered set. We propose a sampling plan called Uniform k-Tuple Partially Rank-Ordered (UKPRSS) where a measurement is collected from each group of a partially rank-ordered set. This article demonstrates estimators from UKPRSS have lower variance than their SRS counterparts. In addition, there is a reduction in the number units needing to be screened when compared to PROSS. Estimation of the mean and distribution function are investigated theoretically.

摘要

McIntyre 引入的排序集抽样 (RSS) 和其他相关方法，例如部分排序集抽样 (PROSS)，表明包含排序机制产生的估计量比其简单随机样本 (SRS) 具有更低的方差-基于同行。与 RSS 一样，PROSS 只对每个部分排序的集合进行一次测量。我们提出了一个称为统一 k 元组部分排序 (UKPRSS) 的抽样计划，其中从部分排序集的每个组中收集测量值。这篇文章展示了来自 UKPRSS 的估计量比它们的 SRS 对应物具有更低的方差。此外，与 PROSS 相比，需要筛选的单位数量有所减少。从理论上研究了均值和分布函数的估计。