Bayesian Statistics Small Area Estimation

National statistical offices are often required to provide statistical information
at several administrative or small area levels. Having good area level
estimates is important because policies will often be based on this type of information.
In this paper we describe how Bayesian hierarchical models can help in the task of
providing good quality small area estimates. Starting from direct estimates obtained
from survey data, we describe a range of Bayesian hierarchical models
that incorporate different types of random effects and show that these give improved
estimates. Models that synthesise individual and aggregated information
are considered as well. Finally, we highlight some additional applications that further
exploit the estimates produced, such as the classification of areas and how
to approach the problem of missing data.