ABSTRACT
Small area estimation (SAE) has received much attention in recent decades due to increasing demand for reliable small area statistics by policy makers. To develop effective policy on various crucial social, economic and health issues at a local level, policy makers need to use accurate data on those issues collected at small area levels. SAE techniques can produce such reliable estimates for small areas (see, for example, [19, 27] and [29]). Traditionally there are direct
in Bayesian
for SAE and each of these methods is linked with very simple to complex theories, algorithms and models. Overall, the direct SAE method comprises a range of estimators such as the Horvitz-Thompson estimator, generalised regression estimator, modified survey estimator, etc. that are based on the survey-design and derived from very simple statistical theories and uncomplicated formulae, whereas the indirect method encompasses complex methodologies from statistics, economics and geography, among others.
