Statistical models for small area estimatoin

Abstract

Abstract. Small Area Estimation (SAE) is a statistical technique to estimate parameters of sub-population containing small size of samples with adequate precision. This technique is very important to be developed due to the increasing needs of statistic for small domains, such as districts or villages. Some SAE techniques have been developed in Canada, USA, and UE based on real data. We adapted these techniques to produce small area statistic in Indonesia based on national data collected by Badan Pusat Statistik. . In this paper we propose a class of generalized additive mixed model to improve the model of auxiliary data in small area estimation. Moreover since some surveys are carried out periodically so that the estimation could be improved by incorporating both the area and time random effects we also proposed a state space model which accounts for the two random effects.