Bayesian hierarchical models for smoothing in two-phase studies, with application to small area estimation

Michelle Ross and Jon Wakefield

Journal of the Royal Statistical Society Series A, 2015, vol. 178, issue 4, 1009-1023

Abstract: type="main" xml:id="rssa12103-abs-0001">

Two-phase study designs are appealing since they allow for the oversampling of rare subpopulations, which improves efficiency. We describe a Bayesian hierarchical model for the analysis of two-phase data. Such a model is particularly appealing in a spatial setting in which random effects are introduced to model between-area variability. In such a situation, one may be interested in estimating regression coefficients or, in the context of small area estimation, in reconstructing the population totals by strata. The gains in efficiency of the two-phase sampling scheme are compared with standard approaches by using 2011 birth data from the research triangle area of North Carolina. We show that the method proposed can overcome small sample difficulties and improve on existing techniques. We conclude that the two-phase design is an attractive approach for small area estimation.

Date: 2015
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