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http://dx.doi.org/10.5351/KJAS.2014.27.4.605

Shrinkage Small Area Estimation Using a Semiparametric Mixed Model  

Jeong, Seok-Oh (Department of statistics, Hankuk University of Foreign Studies)
Choo, Manho (Department of statistics, Hankuk University of Foreign Studies)
Shin, Key-Il (Department of statistics, Hankuk University of Foreign Studies)
Publication Information
The Korean Journal of Applied Statistics / v.27, no.4, 2014 , pp. 605-617 More about this Journal
Abstract
Small area estimation is a statistical inference method to overcome large variance due to a small sample size allocated in a small area. A shrinkage estimator obtained by minimizing relative error(RE) instead of MSE has been suggested. The estimator takes advantage of good interpretation when the data range is large. A semiparametric estimator is also studied for small area estimation. In this study, we suggest a semiparametric shrinkage small area estimator and compare small area estimators using labor statistics.
Keywords
Spline regression; linear mixed estimation; empirical best linear unbiased predictor; shrinkage estimator;
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Times Cited By KSCI : 4  (Citation Analysis)
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