Small area estimation via unmatched sampling and linking models

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

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


The authors use an empirical Bayes (EB) approach to small area estimation under area-level unmatched sampling and linking models. Model parameters are estimated by a unified expectation and maximization (EM) algorithm and used to obtain EB estimators of area parameters. Results are extended to a nonparametric linking model based on a spline approximation. Approximate EB estimators that are computationally simpler are also obtained. Different bootstrap approaches to estimating the mean squared error (MSE) of the EB estimators are proposed. Results of a simulation study on the performance of the proposed methods are presented. Proposed methods are applied to data from a survey of family income and expenditure in Japan and poverty rates in Spanish provinces.

Original languageEnglish
Pages (from-to)407-427
Number of pages21
Issue number2
Publication statusPublished - 2018 Jun 1
Externally publishedYes


  • Bootstrap
  • Empirical Bayes
  • Expectation–maximization algorithm
  • Fay–Herriot model
  • Mean squared error
  • Penalized spline

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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