Hierarchical Bayes small-area estimation with an unknown link function

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

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)885-897
Number of pages13
JournalScandinavian Journal of Statistics
Volume46
Issue number3
DOIs
Publication statusPublished - 2019
Externally publishedYes

Keywords

  • Fay–Herriot model
  • Markov chain Monte Carlo
  • penalized spline
  • unmatched sampling and linking models

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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