A review of adaptive cluster sampling: 1990–2003
A review of adaptive cluster sampling: 1990–2003
复制标题
自适应整群抽样回顾：1990-2003
DOI：
10.1007/s10651-005-6818-0
复制
发表时间：
2005
影响因子：
3.8
通讯作者：
J. Borkowski
中科院分区：
文献类型：
--
作者：
P. Turk;J. Borkowski
自适应聚类采样（ACS）是一种自适应采样方案，其操作规则是当初始选择的采样单元的观测值满足某些感兴趣的条件C时，在某些预定义的伴随邻域中的其他附加单元也被添加到样本中。反过来，如果这些额外的单元中的任何一个满足C，那么它们对应的单元邻域也被添加到样本中，依此类推。当没有遇到满足C的额外单元时，这个过程停止。本文将对自Thompson（1990）[Journal of the American Statistical Association，85，1050-1059]引入ACS以来的主要发展和问题进行综述。
Adaptive cluster sampling (ACS) is an adaptive sampling scheme which operates under the rule that when the observed value of an initially selected sampling unit satisfies some condition of interest, C, other additional units in some pre-defined accompanying neighborhood are also added to the sample. In turn, if any of these additional units satisfy C, then their corresponding unit neighborhoods are added to the sample as well, and so on. This process stops when no additional units satisfying C are encountered. This paper will provide a review of the major developments and issues in ACS since its introduction by Thompson (1990) [Journal of the American Statistical Association, 85, 1050–1059].