Adaptive cluster sampling with a data driven stopping rule
Adaptive cluster sampling with a data driven stopping rule
复制标题
具有数据驱动停止规则的自适应聚类采样
DOI：
10.1007/s10260-010-0149-5
复制
发表时间：
2011
影响因子：
1
通讯作者：
T. Battista
中科院分区：
文献类型：
--
作者：
S. A. Gattone;T. Battista
自适应整群抽样是一种适用于稀有聚类群的抽样设计。在环境和生态应用中，生物种群通常是空间分布高度斑块的动物或植物。然而，当研究人群并不罕见且聚集程度较低时，由于最终样本量可能很容易失控，ACS的设计效率会较低。为了提高蚁群算法相对于简单随机抽样(SRS)的精度和成本，提出了一种新的蚁群算法。其思想是通过数据驱动的停止规则来检测最佳样本大小，以便确定何时停止自适应过程。通过引入停止规则，不尊重蚁群算法的理论基础，并用蒙特卡罗模拟方法研究了蚁群算法中常用估值器的行为。结果表明，当种群的聚集度低于预期时，提出的蚁群算法能够控制有效样本量，并防止蚁群算法典型的过度效率损失。特别是在没有关于人口结构的先验信息的情况下，可以建议采用拟议的战略，因为它不需要事先了解有关人口的稀有程度和聚集性。
The adaptive cluster sampling (ACS) is a suitable sampling design for rare and clustered populations. In environmental and ecological applications, biological populations are generally animals or plants with highly patchy spatial distribution. However, ACS would be a less efficient design when the study population is not rare with low aggregation since the final sample size could be easily out of control. In this paper, a new variant of ACS is proposed in order to improve the performance (in term of precision and cost) of ACS versus simple random sampling (SRS). The idea is to detect the optimal sample size by means of a data-driven stopping rule in order to determine when to stop the adaptive procedure. By introducing a stopping rule the theoretical basis of ACS are not respected and the behaviour of the ordinary estimators used in ACS is explored by using Monte Carlo simulations. Results show that the proposed variant of ACS allows to control the effective sample size and to prevent from excessive efficiency loss typical of ACS when the population is less clustered than anticipated. The proposed strategy may be recommended especially when no prior information about the population structure is available as it does not require a prior knowledge of the degree of rarity and clustering of the population of interest.