A spatial unit level model for small area estimation

* This paper approaches the problem of small area estimation in the framework of spatially correlated data. We propose a class of estimators allowing the integration of sample information of a spatial nature. Those estimators are based on linear models with spatially correlated small area effects wh...

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Bibliographic Details
Published inRevstat Vol. 9; no. 2; p. 155
Main Authors Coelho, Pedro S, Pereira, Luis N
Format Journal Article
LanguageEnglish
Published Instituto Nacional de Estatistica 01.06.2011
Instituto Nacional de Estatística | Statistics Portugal
Subjects
Analysis
combined estimator
empirical best linear unbiased prediction
Geospatial data
Monte Carlo method
small area estimation
spatial models
Statistical models
unit level models
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ISSN1645-6726
2183-0371
DOI10.57805/revstat.v9i2.102

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Summary:* This paper approaches the problem of small area estimation in the framework of spatially correlated data. We propose a class of estimators allowing the integration of sample information of a spatial nature. Those estimators are based on linear models with spatially correlated small area effects where the neighbourhood structure is a function of the distance between small areas. Within a Monte Carlo simulation study we analyze the merits of the proposed estimators in comparison to several traditional estimators. We conclude that the proposed estimators can compete in precision with competitive estimators, while allowing significant reductions in bias. Their merits are particularly conspicuous when analyzing their conditional properties. Key-Words: * combined estimator; empirical best linear unbiased prediction; small area estimation; spatial models; unit level models. AMS Subject Classification: * 62D05, 62F40, 62J05.
ISSN:1645-6726
2183-0371
DOI:10.57805/revstat.v9i2.102