Open AccessEstimation of Education Indicators in East Java Using Multivariate Fay-Herriot Model
Author(s) -
Novia Permatasari,
Azka Ubaidillah
Publication year - 2022
Publication title -
proceedings of international conference on data science and official statistics
Language(s) - English
Resource type - Journals
ISSN - 2809-9842
DOI - 10.34123/icdsos.v2021i1.51
Subject(s) - univariate , multivariate statistics , statistics , multivariate analysis , estimation , sample (material) , econometrics , mathematics , computer science , engineering , chemistry , systems engineering , chromatography
Education is an important aspect in improving human resources. Data availability of education indicators in a low administrative level is needed as a basis for education planning in that region. The problem of sample size when provide a low administrative level data can be overcome by indirect estimation, namely Small Area Estimation (SAE). SAE is able to increase the effectiveness of the survey sample size by using the strength of neighbouring areas and information from auxiliary variables related to the variables of interest. We obtain simulation study to compare multivariate model to univariate model and implement multivariate model to estimate three education indicators which are obtained from the National Socio-Economic Surveys by Statistics Indonesia. Simulation results are in line with previous studies, where the multivariate Fay-Herriot model with p variable has smaller of mean squares error (MSE) than the univariate model. The model implementation to estimate CrudeParticipation Rate (APK), School Participation Rate (APS), and Pure Participation Rate (APM) also shows that the multivariate model produces smaller RRMSE than the direct estimates. It can be concluded that multivariate model is able to produce more efficient estimates than direct estimation and univariate model.