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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/23102

Title: Model-based inference for small area estimation with sampling weights
Authors: Vandendijck, Yannick
Faes, Christel
Kirby, Russel S.
Lawson, Andrew B.
Hens, Niel
Issue Date: 2016
Citation: Spatial Statistics, 18(B), p. 455-473
Abstract: 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.
Notes: Vandendijck, Y (reprint author), Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, Diepenbeek, Belgium. yannick.vandendijck@uhasselt.be
URI: http://hdl.handle.net/1942/23102
Link to publication: http://www.sciencedirect.com/science/article/pii/S2211675316300690
DOI: 10.1016/j.spasta.2016.09.004
ISI #: 000393232900009
ISSN: 2211-6753
Category: A1
Type: Journal Contribution
Validation: ecoom, 2018
Appears in Collections: Research publications

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