平滑模型辅助的小面积比例估计

Pub Date : 2023-07-30 DOI:10.1002/cjs.11787
Peter A. Gao, Jon Wakefield
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引用次数: 0

摘要

在人口普查数据有限的国家,准确估算国家以下各级的卫生和人口指标具有挑战性。现有的基于模型的地理统计方法利用协变量信息和空间平滑来降低估算值的变异性,但往往忽略了调查设计,而传统的小区域估算方法可能无法以设计一致的方式同时纳入单位层面的协变量信息和空间平滑。我们提出了一种平滑模型辅助估算器,它考虑了调查设计,并同时利用了单位级协变量和空间平滑。在一定的规则性假设下,该估计器既符合设计,又符合模型。我们使用真实数据和模拟数据将其与现有的基于设计和基于模型的估计器进行了比较。
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Smoothed model-assisted small area estimation of proportions

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 the 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 regularity 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.

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