Nonstationary EBLUP on Prediction of Poverty Rate at Village Level in Lembata Regency

Abstract

The village development program requires accurate village level data, such as the poverty rate. However data poverty rate in Indonesia can only be obtained at the regency/municipality level. An analysis technique to overcome this problem is Small Area Estimation (SAE). SAE model related to poverty rate must be able to produce an estimated proportion that is in the interval of 0 and 1. One approach that can be done is to use logit transformation. The purpose of this study was to estimate the poverty rate at village level in Lembata Regency, Nusa Tenggara Timur Province. This estimation was done by comparing the Empirical Best Linear Unbiased Prediction (EBLUP), Spatial Empirical Best Linear Unbiased Prediction (SEBLUP), and Nonstationary Empirical Best Linear Unbiased Prediction (NSEBLUP). The results showed that logit transformations produced estimates between 0 and 1. The best method to estimate poverty rate at village level in Lembata Regency was NSEBLUP, which produced estimation that more precise than EBLUP and SEBLUP.