Research Article
Estimating the Poverty level in the Coastal Areas of Mukomuko District Using Small Area Estimation: Empirical Best Linear Unbiased Prediction Method
@INPROCEEDINGS{10.4108/eai.2-8-2019.2290477, author={Etis Sunandi and Dian Agustina and Herlin Fransiska}, title={Estimating the Poverty level in the Coastal Areas of Mukomuko District Using Small Area Estimation: Empirical Best Linear Unbiased Prediction Method}, proceedings={Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia}, publisher={EAI}, proceedings_a={ICSA}, year={2020}, month={1}, keywords={coastal area eblup poverty data small area estimation}, doi={10.4108/eai.2-8-2019.2290477} }- Etis Sunandi
Dian Agustina
Herlin Fransiska
Year: 2020
Estimating the Poverty level in the Coastal Areas of Mukomuko District Using Small Area Estimation: Empirical Best Linear Unbiased Prediction Method
ICSA
EAI
DOI: 10.4108/eai.2-8-2019.2290477
Abstract
This research aims to estimate the poverty level in the Coastal Areas of Mukomuko District using small area estimation. One of the estimation methods on small area estimation is Empirical Best Linear Unbiased Prediction (EBLUP). using the method, the poverty estimator in the coastal area of Mukomuko District is obtained. The evaluation of parameter estimator is calculated by the value of MSE (Mean Square Error) using Bootstrap resampling method. Based on the result of the study is seen that the MSE value of EBLUP estimators is smaller than the MSE value of the direct estimator in each village. The MSE value of the EBLUP estimators is smaller than the MSE value from the direct estimator for each village. This indicates that the estimation with the EBLUP method can improve the estimation of parameters.