论文标题
平滑模型辅助小面积估计
Smoothed Model-Assisted Small Area Estimation
论文作者
论文摘要
在人口人口普查数据有限的国家中，对健康和人口统计指标的准确次数估计值都具有挑战性。现有的基于模型的地统计学方法利用协变量信息和空间平滑来降低估计值的可变性，但通常忽略了调查设计，而传统的小面积估计方法可能不会以设计一致的方式包含单位级别的协变量信息和空间平滑。我们提出了一个平滑的模型辅助估计器，该估计值是对调查设计的解释，并利用单位级别的协变量和空间平滑。在某些假设下，该估计器既具有设计一致性又符合模型。我们将其与现有的基于设计和模型的估计器使用真实数据和基于模型的估计器进行了比较。
In countries where population census data are limited, generating accurate subnational estimates of health and demographic indicators is challenging. Existing model-based geostatistical methods leverage covariate information and spatial smoothing to reduce the variability of estimates but often ignore survey design, while traditional small area estimation approaches may not incorporate both unit level covariate information and spatial smoothing in a design-consistent way. We propose a smoothed model-assisted estimator that accounts for survey design and leverages both unit level covariates and spatial smoothing. Under certain assumptions, this estimator is both design-consistent and model-consistent. We compare it with existing design-based and model-based estimators using real and simulated data.
