Statistical inference based on complex survey designs using rank information and order statistics

在许多实验中，实现统计上合理的抽样设计的一个重要因素是从抽样单位进行测量的成本。有时，研究人员可以获得一些辅助信息，或早期调查的结果，专家意见知识，或来自人群的廉价和有用的测量，这些信息可以在对抽样单位进行最终测量之前轻松地对其进行排序。例如，考虑人口中骨矿物质密度的估计问题。这种研究的对象很多，但是通过双x线吸收仪对选定的对象进行骨矿物质密度测量是昂贵的。因此，重要的是在不减少获得有关人口骨矿物质密度构成的可靠信息的情况下，尽量减少此类研究所需的测量次数。一个成功的策略是利用大量采样单位的专家意见知识，从人群中确定更具代表性的样本，从这些样本中，应该通过双x射线吸收仪收集昂贵的骨密度测量。基于秩的抽样设计提供了一系列技术，可以在廉价信息的帮助下获得和分析这些昂贵的测量结果。这些设计已经应用于渔业、农业、环境和生态研究，如辐射（土壤污染和疾病集群）或污染（水污染和作物根病），以及医学研究、机器学习和图像处理。

In many experiments, an important factor in implementing a statistically sound sampling design is the cost of taking measurements from sampling units. Sometimes researchers have access to some auxiliary information, or results of earlier surveys, expert-opinion knowledge, or inexpensive and useful measurements from the population that can be used to easily rank sampling units prior to taking final measurements on them. For example, consider the problem of estimation of bone mineral density in a human population. Subjects for such a study are plentiful, but measurement of bone mineral density via dual x-ray absorptiometry on the selected subjects is expensive. Thus, it is important to minimize the number of measurements required for such a study without reducing the amount of reliable information obtained about the bone mineral density makeup of the population. One successful strategy is to use expert-opinion knowledge on a large number of sampling units to identify more representative samples from the population from which the expensive bone mineral density measurement should be collected via dual x-ray absorptiometry. Rank-based sampling  designs provide a collection of techniques to obtain and analyze these expensive measurements with the help of inexpensive information.  These designs have found applications in fisheries, agricultural, environmental and ecological studies such as radiation (soil contamination and disease clusters) or pollution (water contamination and root disease of crops)  as well as in medical research, machine learning and image processing.