Model-based inference for small area estimation with sampling weights

作     者：Vandendijck, Y. Faes, C. Kirby, R. S. Lawson, A. Hens, N. 

作者机构：Hasselt Univ Interuniv Inst Biostat & Stat Bioinformat Diepenbeek Belgium Univ S Florida Coll Publ Hlth Dept Community & Family Hlth Tampa FL USA Univ South Carolina Dept Publ Hlth Charleston SC USA Univ Antwerp Ctr Hlth Econ Res & Modeling Infect Dis Vaccine & Infect Dis Inst Antwerp Belgium 

出 版 物：《SPATIAL STATISTICS》 (空间统计)

年 卷 期：2016年第18卷第PartB期

页      面：455-473页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 

基　　金：Hasselt University [BOF11D04FAEC] National Institutes of Health [R01CA172805] University of Antwerp scientific chair in Evidence-Based Vaccinology IAP Research Network P7/06 of the Belgian State (Belgian Science Policy) [FEDRA P7/06] NIH R01CA172805 

主　　题：Integrated nested Laplace approximations Model-based inference Small area estimation Spatial smoothing Survey weighting 

摘      要：Obtaining reliable estimates about health outcomes for areas or domains where only few to no samples are available is the goal of small area estimation (SAE). Often, we rely on health surveys to obtain information about health outcomes. Such surveys are often characterised by a complex design, stratification, and unequal sampling weights as common features. Hierarchical Bayesian models are well recognised in SAE as a spatial smoothing method, but often ignore the sampling weights that reflect the complex sampling design. In this paper, we focus on data obtained from a health survey where the sampling weights of the sampled individuals are the only information available about the design. We develop a predictive model-based approach to estimate the prevalence of a binary outcome for both the sampled and non-sampled individuals, using hierarchical Bayesian models that take into account the sampling weights. A simulation study is carried out to compare the performance of our proposed method with other established methods. The results indicate that our proposed method achieves great reductions in mean squared error when compared with standard approaches. It performs equally well or better when compared with more elaborate methods when there is a relationship between the responses and the sampling weights. The proposed method is applied to estimate asthma prevalence across districts. (C) 2016 Elsevier B.V. All rights reserved.