Shrinkage Small Area Estimation Using a Semiparametric Mixed Model

被引：0

作者：

Jeong, Seok-Oh ^{[1
]}

Choo, Manho ^{[1
]}

Shin, Key-Il ^{[1
]}

机构：

[1] Hankuk Univ Foreign Studies, Dept Stat, Yongin 449791, Gyeonggi, South Korea

来源：

KOREAN JOURNAL OF APPLIED STATISTICS | 2014年 / 27卷 / 04期

关键词：

Spline regression; linear mixed estimation; empirical best linear unbiased predictor; shrinkage estimator;

D O I：

10.5351/KJAS.2014.27.4.605

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：605 / 617

页数：13

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