Ranked set sampling
Ranked set sampling
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DOI：
10.1002/wics.92
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发表时间：
2010-07
期刊：
影响因子：
--
通讯作者：
D. Wolfe
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文献类型：
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作者：
D. Wolfe
从总体中收集数据的最常见抽样方法是简单随机抽样（SRS），其目的是对总体的未知特征进行推断。有一个概率保证，SRS中的每个测量的观察可以被认为是代表人口。尽管有这一保证，但仍然存在一种明显的可能性，即特定的SRS可能无法提供真正具有代表性的人口情况。考虑到这一点，统计学家们开发了各种方法来防止获得这种不具代表性的样本。抽样设计，如分层抽样、概率抽样和整群抽样，都在抽样过程中提供了额外的结构，以提高所收集的样本数据确实提供了良好的总体代表性的可能性。大多数数据收集环境的第二个目标是最大限度地减少与获取数据相关的成本。排序集抽样（RSS）是一个相对较新的发展，解决了这两个问题。它使用来自人口的额外信息，为数据收集过程提供更多的结构，并减少不具代表性的样本的可能性。此外，它的目的是最大限度地减少所需的测量观测的数量，以达到所需的精度进行推断。在这篇文章中，我们提供了对平衡和非平衡RSS的一般性介绍，描述了收集每种类型RSS的基本方法和一些相关属性。我们讨论了一些重要的因素，影响RSS程序的性能。版权所有© 2010年约翰威利父子公司。
The most common sampling approach for collecting data from a population with the goal of making inferences about unknown features of the population is a simple random sample (SRS). There is a probabilistic guarantee that each measured observation in an SRS can be considered representative of the population. Despite this assurance, there remains a distinct possibility that a specific SRS might not provide a truly representative picture of the population. With that in mind, statisticians have developed a variety of ways to guard against obtaining such unrepresentative samples. Sampling designs such as stratified sampling, probability sampling, and cluster sampling all provide additional structure on the sampling process to improve the likelihood that the collected sample data do, indeed, provide a good representation of the population. A secondary goal in most data collection settings is to minimize the costs associated with obtaining the data. Ranked set sampling (RSS) is a relatively recent development that addresses both of these issues. It uses additional information from the population to provide more structure to the data collection process and decreases the likelihood of an unrepresentative sample. In addition, it is designed to minimize the number of measured observations required to achieve the desired precision in making inferences. In this article, we provide a general introduction to both balanced and unbalanced RSS, describing the basic approaches for collecting each type of RSS and some of the associated properties. We discuss a number of important factors that affect the performance of RSS procedures. Copyright © 2010 John Wiley & Sons, Inc.