Small area estimation combining information from several sources

Date
2015-06-01
Authors
Kim, Jae Kwang
Park, Seunghwan
Kim, Jae Kwang
Kim, Seo-young
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Altmetrics
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Statistics
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Statistics
Abstract

An area-level model approach to combining information from several sources is considered in the context of small area estimation. At each small area, several estimates are computed and linked through a system of structural error models. The best linear unbiased predictor of the small area parameter can be computed by the general least squares method. Parameters in the structural error models are estimated using the theory of measurement error models. Estimation of mean squared errors is also discussed. The proposed method is applied to the real problem of labor force surveys in Korea.

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This article is published as Kim, Jae-kwang, Seunghwan Park, and Seo-young Kim. "Small area estimation combining information from several sources." Survey Methodology 41 (2015). Posted with permission.

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