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Small Area Prediction under Alternative Model Specifications

100%
Erciulescu A. L., Fuller W. A.
Statistics in Transition new series
|
2016
|
vol. 17
|
issue 1
9-24
EN
Construction of small area predictors and estimation of the prediction mean squared error, given different types of auxiliary information are illustrated for a unit level model. Of interest are situations where the mean and variance of an auxiliary variable are subject to estimation error. Fixed and random specifications for the auxiliary variables are considered. The efficiency gains associated with the random specification for the auxiliary variable measured with error are demonstrated. A parametric bootstrap procedure is proposed for the mean squared error of the predictor based on a logit model. The proposed bootstrap procedure has smaller bootstrap error than a classical double bootstrap procedure with the same number of samples.
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On the Simulation Study of Jackknife and Bootstrap MSE Estimators of a Domain Mean Predictor for Fay‑Herriot Model

51%
Krzciuk M. K.
Acta Universitatis Lodziensis. Folia Oeconomica
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2017
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vol. 5
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issue 331
169-183
PL
W artykule rozważany jest problem estymacji błędu średniokwadratowego (MSE) w przypadku predykcji wartości średniej w domenie, w oparciu o model Faya-Herriota. W badaniu symulacyjnym analizowane są własności ośmiu estymatorów MSE, w tym bazujących na metodzie jackknife (Jiang, Lahiri, Wan (2002), Chen, Lahiri (2002, 2003)) oraz parametrycznej metodzie bootstrap (Gonzalez-Manteiga et al. (2008), Buthar, Lahiri (2003)). W modelu Faya-Herriota zakładana jest niezależność składników losowych, a obciążenia estymatorów MSE są małe dla dużej liczby domen. Celem artykułu jest porównanie własności estymatorów MSE przy różnej liczbie domen i błędnej specyfikacji modelu wynikającej z występowania korelacji efektów losowych w badaniu symulacyjnym.
EN
  We consider the problem of the estimation of the mean squared error (MSE) of some domain mean predictor for Fay‑Herriot model. In the simulation study we analyze properties of eight MSE estimators including estimators based on the jackknife method (Jiang, Lahiri, Wan, 2002; Chen, Lahiri, 2002; 2003) and parametric bootstrap (Gonzalez‑Manteiga et al., 2008; Buthar, Lahiri, 2003). In the standard Fay‑Herriot model the independence of random effects is assumed, and the biases of the MSE estimators are small for large number of domains. The aim of the paper is the comparison of the properties of MSE estimators for different number of domains and the misspecification of the model due to the correlation of random effects in the simulation study.  
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