Empirical best prediction of small area bivariate parameters
View/ Open
Use this link to cite
http://hdl.handle.net/2183/32261Collections
- GI-MODES - Artigos [122]
Metadata
Show full item recordTitle
Empirical best prediction of small area bivariate parametersAuthor(s)
Date
2022Citation
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.
Abstract
[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.
Keywords
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
Editor version
Rights
Atribución-NoComercial 3.0 España
ISSN
0303-6898
1467-9469
1467-9469