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Mercer L, Wakefield J, Chen C, et al. A comparison of spatial smoothing methods for small area estimation with sampling weights. Spat Stat. 2014;8:69-85doi: 10.1016/j.spasta.2013.12.001.
Mercer, L., Wakefield, J., Chen, C., & Lumley, T. (2014). A comparison of spatial smoothing methods for small area estimation with sampling weights. Spatial statistics, 869-85. https://doi.org/10.1016/j.spasta.2013.12.001
Mercer, Laina, et al. "A comparison of spatial smoothing methods for small area estimation with sampling weights." Spatial statistics vol. 8 (2014): 69-85. doi: https://doi.org/10.1016/j.spasta.2013.12.001
Mercer L, Wakefield J, Chen C, Lumley T. A comparison of spatial smoothing methods for small area estimation with sampling weights. Spat Stat. 2014 May 01;8:69-85. doi: 10.1016/j.spasta.2013.12.001. PMID: 24959396; PMCID: PMC4064473.
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Spat Stat. 2014 May 01;8:69-85. doi: 10.1016/j.spasta.2013.12.001.

A comparison of spatial smoothing methods for small area estimation with sampling weights.

Spatial statistics

Laina Mercer, Jon Wakefield, Cici Chen, Thomas Lumley

Affiliations

  1. Department of Statistics, University of Washington, United States.
  2. Department of Biostatistics, Brown University, United States.
  3. Department of Statistics, University of Auckland, New Zealand.

PMID: 24959396 PMCID: PMC4064473 DOI: 10.1016/j.spasta.2013.12.001

Abstract

Small area estimation (SAE) is an important endeavor in many fields and is used for resource allocation by both public health and government organizations. Often, complex surveys are carried out within areas, in which case it is common for the data to consist only of the response of interest and an associated sampling weight, reflecting the design. While it is appealing to use spatial smoothing models, and many approaches have been suggested for this endeavor, it is rare for spatial models to incorporate the weighting scheme, leaving the analysis potentially subject to bias. To examine the properties of various approaches to estimation we carry out a simulation study, looking at bias due to both non-response and non-random sampling. We also carry out SAE of smoking prevalence in Washington State, at the zip code level, using data from the 2006 Behavioral Risk Factor Surveillance System. The computation times for the methods we compare are short, and all approaches are implemented in R using currently available packages.

Keywords: Complex surveys; Design-based inference; Intrinsic CAR models; Random effects models; Weighting

References

  1. Int J Epidemiol. 2009 Apr;38(2):330-6 - PubMed
  2. Biostatistics. 2010 Jul;11(3):397-412 - PubMed

Publication Types

Grant support