Small area estimation via unmatched sampling and linking models

Shonosuke Sugasawa (), Tatsuya Kubokawa and J. N. K. Rao
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Shonosuke Sugasawa: The Institute of Statistical Mathematics
Tatsuya Kubokawa: University of Tokyo
J. N. K. Rao: Carleton University

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2018, vol. 27, issue 2, No 8, 407-427

Abstract: 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.

Keywords: Bootstrap; Empirical Bayes; Expectation–maximization algorithm; Fay–Herriot model; Mean squared error; Penalized spline; 62D05 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11749-017-0551-5

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