Open AccessHierarchical Bayesian Modelling in Small Area for Estimating Binary Data
Author(s) -
Alparslan Sari,
Ferra Yanuar
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1554/1/012049
Subject(s) - small area estimation , estimation , bayesian probability , mean squared error , bayes estimator , logit , statistics , computer science , binary number , bayesian inference , mathematics , data mining , engineering , estimator , arithmetic , systems engineering
Indonesian’s data are obtained from BPS from census, but census are designed for large area. Now, local goverments need to have reliable and detailed information in small area. Direct estimation are unreliable to be applied in small area because produced high mean square error (MSE). To overcome this problem, we use the indirect estimation Small Area Estimation Hierarchical Bayesian (SAE HB) with Logit Normal as the model. From this study founded that HB produced a smaller MSE than direct estimation