AI 中文总结
本文针对小区域估计的非概率样本方法进行综述，引入数据缺陷相关性概念，通过模拟研究评估不同DDC水平下的多种非概率样本方法并扩展其至小区域估计场景。
AI 中文摘要
非概率样本（NPS）因收集成本更低、可提供大得多的样本量，且可能覆盖传统概率调查无法触及的总体而颇具吸引力。随着传统调查的应答率不断下降，调查统计学领域对非概率样本的兴趣迅速增长。这些方法与小区域估计（SAE）尤为相关，小区域估计始终存在对精细地理尺度和详细人口统计域估计值的需求。尽管方法发展迅速，但对于不同条件下哪些方法表现最佳，人们的理解仍然有限。本文综述了非概率样本方法的最新进展，包括数据缺陷相关性（DDC）这一作为数据质量度量和各类非概率样本方法分类工具的概念。随后，我们开展了一项综合模拟研究，在不同的DDC水平下评估一系列非概率样本方法，并将几种现有方法扩展至小区域估计场景。
英文摘要
Nonprobability samples (NPS) are attractive because they are less costly to collect, can provide substantially larger sample sizes, and may reach populations that traditional probability surveys do not. As response rates for traditional surveys fall, interest in NPS has grown rapidly within the field of survey statistics. These methods are especially relevant for small area estimation (SAE), where there is ever-present demand for estimates at fine geographic scales and detailed demographic domains. Despite rapid methodological development, there remains limited understanding of which approaches perform best under different conditions. In this paper, we review recent developments in NPS methodology, including the concept of data defect correlation (DDC) as a measure of data quality and as a tool for categorizing the various NPS methods. We then present a comprehensive simulation study that evaluates a range of NPS approaches under varying levels of DDC and extend several existing methods to the SAE setting.
Comments34 pages, 8 figures
