Small area estimation of average compositions under multivariate nested error regression models
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http://hdl.handle.net/2183/32882
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Small area estimation of average compositions under multivariate nested error regression modelsAutor(es)
Data
2023Cita bibliográfica
M.D. Esteban, M. J. Lombardía, E. López-Vizcaíno, D. Morales, A. & Pérez, "Small area estimation of average compositions under multivariate nested error regression models", Test, 2023, doi:10.1007/s11749-023-00847-0
Resumo
[Abstract]: 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.
Palabras chave
Bootstrap
Compositional data
Household budget survey
Household expenditures
Multivariate nested error regression model
Small area estimation
Compositional data
Household budget survey
Household expenditures
Multivariate nested error regression model
Small area estimation
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Dereitos
Atribución 4.0 Internacional (CC BY 4.0)