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

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  • Michelle Ross
  • Jon Wakefield

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.

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  • Michelle Ross & Jon Wakefield, 2015. "Bayesian hierarchical models for smoothing in two-phase studies, with application to small area estimation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 1009-1023, October.
    • Handle: RePEc:bla:jorssa:v:178:y:2015:i:4:p:1009-1023
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    File URL: http://hdl.handle.net/10.1111/rssa.2015.178.issue-4
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    Cited by:

    1. Chien-Chou Chen & Guo-Jun Lo & Ta-Chien Chan, 2022. "Spatial Analysis on Supply and Demand of Adult Surgical Masks in Taipei Metropolitan Areas in the Early Phase of the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(11), pages 1-12, May.

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