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Suseno Bayu
IPB University
Computer Science & IT

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Journal : PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS

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GLMM and GLMMTree for Modelling Poverty in Indonesia Suseno Bayu; Khairil Anwar Notodiputro; Bagus Sartono
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.333

Abstract

GLMMTree is a tree-based algorithm that can detect interaction and find subgroups in the GLMM to improve fixed effect estimation. This study uses GLMMTree for the actual data applications of poverty in Indonesia and confirms that the GLMMTree algorithm method has better precision than GLMM. The significant predictors that affect poverty in Indonesia are the unemployment rate and the GRDP at a constant price. GLMMTree algorithm enriches the analysis by finding subgroups of provinces with electricity lighting access and clean drinking water sources variables.
Co-Authors Bagus Sartono Khairil Anwar Notodiputro