Small area estimation via unmatched sampling and linking models

• Shonosuke Sugasawa ^[3] ; Tatsuya Kubokawa ^[1] ; J. N. K. Rao ^[2]
  1. [1] University of Tokyo

     University of Tokyo

     Japón

     
  2. [2] Carleton University

     Carleton University

     Canadá

     
  3. [3] The Institute of Statistical Mathematics

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• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 27, Nº. 2, 2018, págs. 407-427
• Idioma: inglés
• DOI: 10.1007/s11749-017-0551-5
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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.