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
收藏
页码：671 / 685
页数：15
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