A Comprehensive Overview of Unit-Level Modeling of Survey Data for Small Area Estimation Under Informative Sampling
A Comprehensive Overview of Unit-Level Modeling of Survey Data for Small Area Estimation Under Informative Sampling
复制标题
信息抽样下小面积估算的调查数据单元级建模综合概述
DOI：
10.1093/jssam/smad020
复制
发表时间：
2023
影响因子：
2.1
通讯作者：
Holan, Scott H
中科院分区：
文献类型：
--
作者：
Parker, Paul A;Janicki, Ryan;Holan, Scott H
基于模型的小区域估计经常与调查数据结合使用，以建立对采样不足或未采样地理区域的估计。这些模型可以在地区一级或单位一级具体说明，但单位一级的模型往往具有潜在的优势，如更精确的估计和容易的空间汇总。然而，相对于地区一级的模型，关于单位一级模型的文献不太普遍。在单元一级对小区域进行建模时，由于用于收集调查数据的信息性抽样机制，经常会出现挑战。本文对信息抽样下的单元级模型进行了全面的方法论回顾，重点是贝叶斯方法。
Model-based small area estimation is frequently used in conjunction with survey data to establish estimates for under-sampled or unsampled geographies. These models can be specified at either the area-level, or the unit-level, but unit-level models often offer potential advantages such as more precise estimates and easy spatial aggregation. Nevertheless, relative to area-level models, literature on unit-level models is less prevalent. In modeling small areas at the unit level, challenges often arise as a consequence of the informative sampling mechanism used to collect the survey data. This article provides a comprehensive methodological review for unit-level models under informative sampling, with an emphasis on Bayesian approaches.