Benchmarking small area estimators

被引：29

作者：

Bell, W. R. ^{[1
]}

Datta, G. S. ^{[2
]}

Ghosh, M. ^{[3
]}

机构：

[1] US Bur Census, Res & Methodol Directorate, Washington, DC 20233 USA

[2] Univ Georgia, Dept Stat, Athens, GA 30602 USA

[3] Univ Florida, Dept Stat, Gainesville, FL 32611 USA

来源：

BIOMETRIKA | 2013年 / 100卷 / 01期

基金：

美国国家科学基金会;

关键词：

Augmented model; Best linear unbiased predictor; External constraint; Internal constraint; Orthogonal projection; STATE-SPACE MODELS; TIME-SERIES; PREDICTION; ERROR;

D O I：

10.1093/biomet/ass063

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

This paper considers benchmarking issues in the context of small area estimation. We find optimal estimators within the class of benchmarked linear estimators under linear constraints. This extends existing results for external and internal benchmarking, and also links the two. Necessary and sufficient conditions for self-benchmarking are found for an augmented model. Most results of this paper are found using ideas of orthogonal projection.

引用

页码：189 / 202

页数：14

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