Model-based small area estimation relies on mixed effects regression models that link the small areas and borrow strength from similar domains. When the auxiliary variables that are used in the models are measured with error, small area estimators that ignore the measurement error may be worse than direct estimators. Alternative small area estimators accounting for measurement error have been proposed in the literature but only for continuous auxiliary variables. Adopting a Bayesian approach, we extend the unit level model to account for measurement error in both continuous and categorical covariates. For the discrete variables we model the misclassification probabilities and estimate them jointly with all the unknown model parameters. We test our model through a simulation study. The effect of the model proposed is emphasized through application to data from the Ethiopia Demographic and Health Survey where we focus on the women's malnutrition issue: a dramatic problem in developing countries and an important indicator of the socio-economic progress of a country.

A unit level small area model with misclassified covariates / Arima, S.; Polettini, S.. - In: JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A. STATISTICS IN SOCIETY. - ISSN 0964-1998. - (2019). [10.1111/rssa.12468]

A unit level small area model with misclassified covariates

Arima S.
;
Polettini S.
2019

Abstract

Model-based small area estimation relies on mixed effects regression models that link the small areas and borrow strength from similar domains. When the auxiliary variables that are used in the models are measured with error, small area estimators that ignore the measurement error may be worse than direct estimators. Alternative small area estimators accounting for measurement error have been proposed in the literature but only for continuous auxiliary variables. Adopting a Bayesian approach, we extend the unit level model to account for measurement error in both continuous and categorical covariates. For the discrete variables we model the misclassification probabilities and estimate them jointly with all the unknown model parameters. We test our model through a simulation study. The effect of the model proposed is emphasized through application to data from the Ethiopia Demographic and Health Survey where we focus on the women's malnutrition issue: a dramatic problem in developing countries and an important indicator of the socio-economic progress of a country.
2019
Bayesian hierarchical model; Markov chain Monte Carlo sampling; Measurement error; Misclassification matrix; Small area estimation
01 Pubblicazione su rivista::01a Articolo in rivista
A unit level small area model with misclassified covariates / Arima, S.; Polettini, S.. - In: JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A. STATISTICS IN SOCIETY. - ISSN 0964-1998. - (2019). [10.1111/rssa.12468]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1289537
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