Small area estimation of per capita expenditures using robust empirical best linear unbiased prediction (REBLUP)

Abstract

A small area is an area with small sample size to estimate parameters in survey sampling. The direct estimation will produce inaccurate estimation since the sample size is not enough to produce estimation with acceptable precision. Small Area Estimation (SAE) is a solution to obtain more precise estimation in a small area. A well-known method in SAE is an empirical best linear unbiased prediction (EBLUP). EBLUP is the estimator of small area means. It will provide an accurate estimation under normality assumptions but it can be sensitive when the data are contaminated by outliers. In this article, we discussed a resistant method in SAE, i.e. robust empirical best linear unbiased prediction (REBLUP). We apply REBLUP from unit-level models to the data obtained from the National Socio-economic Survey (SUSENAS). The means of per capita expenditures are calculated for all small areas. We compare the estimates of per capita expenditure in the small area using direct estimation, EBLUP and REBLUP methods using data that contain outliers. The result shows that REBLUP estimation has produced more accurate estimates when compared to the other methods.