An Applied Comparison of Area-Level Linear Mixed Models in Small Area Estimation

被引：0

作者：

Pereira, Luis Nobre ^{[1
,2
]}

Coelho, Pedro Simoes ^{[3
,4
]}

机构：

[1] Univ Algarve, Escola Super Gestao Hotelaria & Turismo, Faro, Portugal

[2] Univ Algarve, Ctr Invest Espaco & Org, Faro, Portugal

[3] Univ Nova Lisboa, Inst Super Estat & Gestao Informacao, P-1200 Lisbon, Portugal

[4] Univ Ljubljana, Fac Econ, Ljubljana, Slovenia

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2013年 / 42卷 / 03期

关键词：

Empirical best linear unbiased prediction; Linear mixed model; Small area estimation; Spatial correlation; Temporal autocorrelation; CROSS-SECTIONAL DATA; MEAN SQUARED ERROR; TIME-SERIES; 4-PERSON FAMILIES; BAYES ESTIMATION; MEDIAN INCOME; UNCERTAINTY; PREDICTION; EBLUP;

D O I：

10.1080/03610918.2011.654029

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：

This article reviews four area-level linear mixed models that borrow strength by exploiting the possible correlation among the neighboring areas or/and past time periods. Its main goal is to study if there are efficiency gains when a spatial dependence or/and a temporal autocorrelation among random-area effects are included into the models. The Fay-Herriot estimator is used as benchmark. A design-based simulation study based on real data collected from a longitudinal survey conducted by a statistical office is presented. Our results show that models that explore both spatial and chronological association considerably improve the efficiency of small area estimates.

引用

页码：671 / 685

页数：15

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