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Robust estimation of mean squared prediction error in small-area estimation
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Robust estimation of mean squared prediction error in small-area estimation
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Journal Article
Robust estimation of mean squared prediction error in small-area estimation
2021
Overview
The nested-error regression model is one of the best-known models in small area estimation. A small area mean is often expressed as a linear combination of fixed effects and realized values of random effects. In such analyses, prediction is made by borrowing strength from other related areas or sources and mean-squared prediction error (MSPE) is often used as a measure of uncertainty. In this article, we propose a bias-corrected analytical estimation of MSPE as well as a moment-match jackknife method to estimate the MSPE without specific assumptions about the distributions of the data. Theoretical and empirical studies are carried out to investigate performance of the proposed methods with comparison to existing procedures.
Le modèle de régression à erreur imbriquée est l’un des mieux connus pour l’estimation sur des petits domaines. La moyenne d’un petit domaine est souvent exprimée comme une combinaison linéaire d’effets fixes et de valeurs réalisées d’effets aléatoires. Pour de telles analyses, les prévisions sont effectuées en empruntant de l’information d’autres domaines associés ou d’autres sources, et l’erreur quadratique moyenne de prévision (EQMP) sert souvent à mesurer l’incertitude. Les auteurs proposent une estimation analytique de l’EQMP corrigée pour le biais ainsi qu’une méthode jackknife d’appariement des moments afin d’estimer l’EQMP sans formuler d’hypothèses spécifiques sur la distribution des données. Ils présentent des études théoriques et empiriques comparant la performance des méthodes proposées aux procédures existantes.
Publisher
Wiley,John Wiley & Sons, Inc,Wiley Subscription Services, Inc
