Poverty Mapping Under Area-Level Random Regression Coefficient Poisson Models
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Poverty Mapping Under Area-Level Random Regression Coefficient Poisson ModelsFecha
2023-11Cita bibliográfica
Naomi Diz-Rosales, María José Lombardía, Domingo Morales, Poverty Mapping Under Area-Level Random Regression Coefficient Poisson Models, Journal of Survey Statistics and Methodology, 2023; smad036, https://doi.org/10.1093/jssam/smad036
Resumen
[Abstract]: Under an area-level random regression coefficient Poisson model, this article derives small area predictors of counts and proportions and introduces bootstrap estimators of the mean squared errors (MSEs). The maximum likelihood estimators of the model parameters and the mode predictors of the random effects are calculated by a Laplace approximation algorithm. Simulation experiments are implemented to investigate the behavior of the fitting algorithm, the predictors, and the MSE estimators with and without bias correction. The new statistical methodology is applied to data from the Spanish Living Conditions Survey. The target is to estimate the proportions of women and men under the poverty line by province.
Palabras clave
Bootstrap
Poverty proportion
Random coefficient
Poisson regression models
Small area estimation
Poverty proportion
Random coefficient
Poisson regression models
Small area estimation
Versión del editor
Derechos
Atribución 3.0 España
ISSN
2325-0992