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A unit level small area model with misclassified covariates
Author(s) -
Arima Serena,
Polettini Silvia
Publication year - 2019
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12468
Subject(s) - small area estimation , covariate , categorical variable , estimator , econometrics , statistics , observational error , specification , errors in variables models , bayesian probability , regression , unit (ring theory) , regression analysis , mixed model , estimation , computer science , mathematics , economics , mathematics education , management
Summary 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.