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Small area estimation via unmatched sampling and linking models

Author

Listed:
  • Shonosuke Sugasawa

    (The Institute of Statistical Mathematics)

  • Tatsuya Kubokawa

    (University of Tokyo)

  • J. N. K. Rao

    (Carleton University)

Abstract

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.

Suggested Citation

  • Shonosuke Sugasawa & Tatsuya Kubokawa & J. N. K. Rao, 2018. "Small area estimation via unmatched sampling and linking models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(2), pages 407-427, June.
    • Handle: RePEc:spr:testjl:v:27:y:2018:i:2:d:10.1007_s11749-017-0551-5
    DOI: 10.1007/s11749-017-0551-5
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    References listed on IDEAS

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    Cited by:

    1. Peter A. Gao & Jonathan Wakefield, 2023. "A Spatial Variance‐Smoothing Area Level Model for Small Area Estimation of Demographic Rates," International Statistical Review, International Statistical Institute, vol. 91(3), pages 493-510, December.

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