GLMM and GLMMTree for Modelling Poverty in Indonesia

Authors

  • Suseno Bayu IPB University
  • Khairil Anwar Notodiputro Department of Statistics, Faculty of Mathematics and Natural Sciences, IPB University
  • Bagus Sartono Department of Statistics, Faculty of Mathematics and Natural Sciences, IPB University

DOI:

https://doi.org/10.34123/icdsos.v2023i1.333

Keywords:

GLMM, GLMMTree, Poverty

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.

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Published

2023-12-29

How to Cite

Bayu, S., Notodiputro, K. A., & Sartono, B. (2023). GLMM and GLMMTree for Modelling Poverty in Indonesia. Proceedings of The International Conference on Data Science and Official Statistics, 2023(1), 121–131. https://doi.org/10.34123/icdsos.v2023i1.333

Issue

Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official Statistics (ICDSOS)

Section

Data Science