Efficiency of the Adaptive Cluster Sampling Designs in Estimation of Rare Populations

Adaptive cluster sampling (ACS) has been a very important tool in estimation of population parameters of rare and clustered population. The fundamental idea behind this sampling plan is to decide on an initial sample from a defined population and to keep on sampling within the vicinity of the units that satisfy the condition that at least one characteristic of interest exists in a unit selected in the initial sample. Despite being an important tool for sampling rare and clustered population, adaptive cluster sampling design is unable to control the final sample size when no prior knowledge of the population is available. Thus adaptive cluster sampling with data-driven stopping rule (ACS’) was proposed to control the final sample size when prior knowledge of population structure is not available. This study examined the behavior of the HT, and HH estimator under the ACS design and ACS’ design using artificial population that is designed to have all the characteristics of a rare and clustered population. The efficiencies of the HT and HH estimator were used to determine the most efficient design in estimation of population mean in rare and clustered population. Results of both the simulated data and the real data show that the adaptive cluster sampling with stopping rule is more efficient for estimation of rare and clustered population than ordinary adaptive cluster sampling.

适应性群团抽样技术方法和应用研究进展

从3个方面概述适应性群团抽样技术的研究现状和发展趋势:1)分析影响适应性群团抽样技术效率的主要因素——最初抽样方法的设计、网络的大小和数量、标准值的大小和邻域形式的设计;2)介绍适应性群团抽样的不同设计、试验模拟研究和估计方法;3)概述适应性群团抽样技术在稀疏群团分布的鱼类、鸟类和植被资源的调查应用研究。最后,指出适应性群团抽样技术在生态因子和资源调查应用研究方面的发展趋势。