Empirical Best Predictor for Nested Error Regression Small Area Models

Nested error regression models have played an important role in small area estimation (SAE) especially for deriving indirect or model-based estimates of small area parameters. The models are valid to be employed whenever the auxiliary information is available at level units, as well as the random effect is independent from the sampling error. Furthermore, the models also assume normality the random effects and sampling errors. The standard SAE method, specifically the Empirical Best Linear Unbiased Predictor (EBLUP), which derives the estimates for small area parameters under the nested error regression models, certainly have to satisfy the strictly assumptions. In this paper, we study the Empirical Best Predictor (EBP) which can be utilized for deriving estimates of small area parameters whenever the variable of interest has skewed distributions. We apply the EBP to estimate the poverty incidence and poverty gap for the regions ('kabupaten' and 'kota') in West Java Province – Indonesia.