Empirical best prediction of small area bivariate parameters
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http://hdl.handle.net/2183/32261
Excepto si se señala otra cosa, la licencia del ítem se describe como Atribución-NoComercial 3.0 España
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Empirical best prediction of small area bivariate parametersAutor(es)
Fecha
2022Cita bibliográfica
M. D. Esteban, M. J. Lombardía, E. López-Vizcaíno, D. Morales, and A. Pérez, “Empirical best prediction of small area bivariate parameters,” Scandinavian Journal of Statistics, vol. 49, no. 4, pp. 1699–1727, 2022, doi: 10.1111/sjos.12618.
Resumen
[Abstract]: This paper introduces empirical best predictors of small area bivariate parameters, like ratios of sums or sums of ratios, by assuming that the target unit-level vector follows a bivariate nested error regression model. The corresponding means squared errors are estimated by parametric bootstrap. Several simulation experiments empirically study the behavior of the introduced statistical methodology. An application to real data from the Spanish household budget survey gives estimators of ratios of food household expenditures by provinces.
Palabras clave
best linear unbiased predictors
household budget surveys
multivariate linear mixed models
nested error regression models
ratio estimators
small area estimation
household budget surveys
multivariate linear mixed models
nested error regression models
ratio estimators
small area estimation
Versión del editor
Derechos
Atribución-NoComercial 3.0 España
ISSN
0303-6898
1467-9469
1467-9469