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Combining multiple imperfect data sources for small area estimation: a Bayesian model of provincial fertility rates in Cambodia

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  • Junni L. Zhang
  • John Bryant

Abstract

Demographic estimation becomes a problem of small area estimation when detailed disaggregation leads to small cell counts. The usual difficulties of small area estimation are compounded when the available data sources contain measurement errors. We present a Bayesian approach to the problem of small area estimation with imperfect data sources. The overall model contains separate submodels for underlying demographic processes and for measurement processes. All unknown quantities in the model, including coverage ratios and demographic rates, are estimated jointly via Markov chain Monte Carlo methods. The approach is illustrated using the example of provincial fertility rates in Cambodia.

Suggested Citation

  • Junni L. Zhang & John Bryant, 2019. "Combining multiple imperfect data sources for small area estimation: a Bayesian model of provincial fertility rates in Cambodia," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 3(2), pages 178-185, July.
    • Handle: RePEc:taf:tstfxx:v:3:y:2019:i:2:p:178-185
    DOI: 10.1080/24754269.2019.1658062
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    Cited by:

    1. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.

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