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Hierarchical Bayes small‐area estimation with an unknown link function

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  • Shonosuke Sugasawa
  • Tatsuya Kubokawa
  • J. N. K. Rao

Abstract

Area‐level unmatched sampling and linking models have been widely used as a model‐based method for producing reliable estimates of small‐area means. However, one practical difficulty is the specification of a link function. In this paper, we relax the assumption of a known link function by not specifying its form and estimating it from the data. A penalized‐spline method is adopted for estimating the link function, and a hierarchical Bayes method of estimating area means is developed using a Markov chain Monte Carlo method for posterior computations. Results of simulation studies comparing the proposed method with a conventional approach based on a known link function are presented. In addition, the proposed method is applied to data from the Survey of Family Income and Expenditure in Japan and poverty rates in Spanish provinces.

Suggested Citation

  • Shonosuke Sugasawa & Tatsuya Kubokawa & J. N. K. Rao, 2019. "Hierarchical Bayes small‐area estimation with an unknown link function," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 46(3), pages 885-897, September.
    • Handle: RePEc:bla:scjsta:v:46:y:2019:i:3:p:885-897
    DOI: 10.1111/sjos.12376
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    Cited by:

    1. Yegnanew A. Shiferaw, 2023. "Mapping Disaggregate-Level Agricultural Households in South Africa Using a Hierarchical Bayes Small Area Estimation Approach," Agriculture, MDPI, vol. 13(3), pages 1-17, March.

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