Statistical models are used to produce estimates of demographic and global health indicators in populations with limited data. Such models integrate multiple data sources to produce estimates and forecasts with uncertainty based on model assumptions. Model assumptions can be divided into assumptions that describe latent trends in the indicator of interest versus assumptions on the data generating process of the observed data, conditional on the latent process value. Focusing on the latter, we introduce a class of data models that can be used to combine data from multiple sources with various reporting issues. The proposed data model accounts for sampling errors and differences in observational uncertainty based on survey characteristics. In addition, the data model employs horseshoe priors to produce estimates that are robust to outlying observations. We refer to the data model class as the normal-with-optional-shrinkage (NOS) set up. We illustrate the use of the NOS data model for the estimation of modern contraceptive use and other family planning indicators at the national level for countries globally, using survey data.

翻译：统计模型被用于在数据有限的群体中生成人口与全球健康指标的估计值。此类模型通过整合多源数据，基于模型假设生成带有不确定性的估计值与预测结果。模型假设可分为两类：一类描述目标指标的潜在趋势，另一类则基于潜在过程值，描述观测数据生成过程的假设。聚焦于后者，本文提出一类可用于整合存在各类报告问题的多源数据的数据模型。所提出的数据模型能够根据调查特征，处理抽样误差与观测不确定性的差异。此外，该数据模型采用马蹄先验（horseshoe priors）以生成对异常观测具有稳健性的估计结果。我们将此类数据模型称为带可选收缩的正态（NOS）设定。本文通过使用调查数据，演示了NOS数据模型在全球各国国家层面现代避孕措施使用率及其他计划生育指标的估计中的应用。