A comparison of different methods for small area estimation

Government agencies often provide small area estimates that rely
on available data and some underlying model that helps to provide
estimates in all areas, even in those that were not sampled. Several
models have been well-established for the study of data coming from
small areas.
In this paper we have made a comparison of some of these methods
paying attention to how dierent types of data sets can be employed
eciently and how to deal with the estimation in areas for which no di-
rect individual data area available. We have considered design-based,
regression and EBLUP estimators, which have been tted using both a
likelihood-based and a Bayesian approach. Spatial correlation among
areas has also been considered. As in any study of the performance
of dierent models, we have also compared dierent criteria for model
comparisson and selection.