A unit level small area model with misclassified covariates
A unit level small area model with misclassified covariates
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DOI：
10.1111/rssa.12468
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发表时间：
2019-10-01
影响因子：
2
通讯作者：
Polettini, Silvia
中科院分区：
文献类型：
--
作者：
Arima, Serena;Polettini, Silvia
基于模型的小区域估计依赖于混合效应回归模型，该模型将小区域连接起来，并借鉴相似域的优势。当模型中使用的辅助变量测量有误差时，忽略测量误差的小面积估计可能比直接估计差。文献中提出了考虑测量误差的替代小面积估计，但仅适用于连续辅助变量。采用贝叶斯方法，我们扩展了单位水平模型，以考虑连续和分类协变量的测量误差。对于离散变量，我们建立了误分类概率模型，并结合所有未知模型参数对其进行联合估计。我们通过模拟研究来检验我们的模型。通过对埃塞俄比亚人口与健康调查数据的应用，强调了所提出模型的效果，我们在调查中重点关注妇女营养不良问题：这是发展中国家的一个严重问题，也是一个国家社会经济进步的一个重要指标。
Model-based small area estimation relies on mixed effects regression models that link the small areas and borrow strength from similar domains. When the auxiliary variables that are used in the models are measured with error, small area estimators that ignore the measurement error may be worse than direct estimators. Alternative small area estimators accounting for measurement error have been proposed in the literature but only for continuous auxiliary variables. Adopting a Bayesian approach, we extend the unit level model to account for measurement error in both continuous and categorical covariates. For the discrete variables we model the misclassification probabilities and estimate them jointly with all the unknown model parameters. We test our model through a simulation study. The effect of the model proposed is emphasized through application to data from the Ethiopia Demographic and Health Survey where we focus on the women's malnutrition issue: a dramatic problem in developing countries and an important indicator of the socio-economic progress of a country.