Title: Small area estimation under random regression coefficient models
Abstract: AbstractStatistical agencies are interested to report precise estimates of linear parameters from small areas. This goal can be achieved by using model-based inference. In this sense, random regression coefficient models provide a flexible way of modelling the relationship between the target and the auxiliary variables. Because of this, empirical best linear unbiased predictor (EBLUP) estimates based on these models are introduced. A closed-formula procedure to estimate the mean-squared error of the EBLUP estimators is also given and empirically studied. Results of several simulation studies are reported as well as an application to the estimation of household normalized net annual incomes in the Spanish Living Conditions Survey.Keywords: small area estimationlinear mixed modelsrandom regression coefficient modelsEBLUPmean-squared errorliving conditions survey AcknowledgementsThe authors are grateful to the Czech and the Spanish Governments for their economical support under the grants MSM 6840770039, MTM2009-09473 and MTM2009-06997. The authors also thank the Instituto Nacional de Estadística for providing the Spanish EU-SILC data.
Publication Year: 2012
Publication Date: 2012-05-09
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 11
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