Small area estimation of average compositions under multivariate nested error regression models
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María Dolores Esteban [1] ; María José Lombardía [2] ; Esther López-Vizcaíno [3] ; Domingo Morales [1] ; Agustín Pérez [1]
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[1] Universidad Miguel Hernández de Elche
Universidad Miguel Hernández de Elche
Elche, España
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[2] Universidade da Coruña
Universidade da Coruña
A Coruña, España
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[3] Instituto Galego de Estatística
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- Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 32, Nº. 2, 2023, págs. 651-676
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
This paper investigates the small area estimation of population averages of unit-level compositional data. The new methodology transforms the compositions into vectors of Rm and assumes that the vectors follow a multivariate nested error regression model. Empirical best predictors of domain indicators are derived from the fitted model, and their mean squared errors are estimated by parametric bootstrap. The empirical analysis of the behavior of the introduced predictors is investigated by means of simulation experiments. An application to real data from the Spanish household budget survey is given. The target is to estimate the average of proportions of annual household expenditures on food, housing and others, by Spanish provinces.
