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Journal

## Statistics in Transition new series

2016 | 17 | 1 | 9-24
Article title

### Small Area Prediction under Alternative Model Specifications

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EN
Abstracts
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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9-24
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author
• National Institute of Statistical Sciences and USDA NASS, 1400 Independence Ave. SW, Room 6040 F, Washington, DC 20250
author
• Iowa State University, 1214 Department of Statistics, Ames, IA 50010
References
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• PFEFFERMANN, D., CORREA, S., (2012). Empirical bootstrap bias correction and estimation of prediction mean square error in small area estimation, Biometrika, 99, 457−472.
• TORABI, M., DATTA, G., RAO, J. N. K., (2009). Empirical Bayes Estimation of Small Area Means under a Nested Error Linear Regression Model with Measurement Errors in the Covariates, Scandinavian Journal of Statistics, 36, 355−368.
• WANG, J., FULLER, W. A., (2003). The mean squared error of small area spedictors constructed with estimated area variances, Journal of the American Statistical Association, 98, 716−723.
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