ANALYTICAL APPROACH OF GENERALIZED LINEAR MODELS FOR HANDLING OVERDISPERSION IN POVERTY DATA OF INDONESIA

  • Restu Arisanti Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Indonesia https://orcid.org/0000-0001-9601-9731
  • Resa Septiani Pontoh Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Indonesia https://orcid.org/0000-0001-9601-9731
  • Sri Winarni Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Indonesia https://orcid.org/0009-0009-6391-2051
  • Fellita Odelia Wibowo Bachelor Degree of Statistics Department, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Indonesia https://orcid.org/0009-0004-6359-5440
  • Hanifah Khairunnisa Bachelor Degree of Statistics Department, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Indonesia https://orcid.org/0009-0008-8378-1223
  • Raissheva Andika Pratama Bachelor Degree of Statistics Department, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Indonesia https://orcid.org/0009-0002-7614-3625
DOI: https://doi.org/10.30598/barekengvol19iss3pp1575-1586
Keywords: Generalized Poisson Regression, Negative Binomial Regression, Overdispersion, Poverty

Abstract

Poverty is one of the complex phenomena that occurs in Indonesia. Various socio-economic variables in Indonesia influence poverty, which we can mathematically model using the Generalized Linear Model (GLM) framework. In this study, we modeled data on the number of poor people per province in 2023 taken from the Badan Pusat Statistik of Indonesia website. The response variable in this study was initially assumed to exhibit equidispersion, where the variance equals the mean. However, the observed variance exceeded the mean, indicating overdispersion. Consequently, Negative Binomial Regression, an extension of the GLM that introduces an additional dispersion parameter, was applied to account for this overdispersion. This approach accommodates overdispersed count data by incorporating a gamma-distributed latent variable. The aim of this study is to determine the best model using Negative Binomial Regression in handling overdispersion in Indonesia's poverty data. This model was chosen for its robustness in capturing increased data variability, enabling the identification of factors that influence poverty. The results of this study offer a mathematically rigorous approach to better understand the underlying dynamics of poverty across provinces in Indonesia.

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Published
2025-07-01
How to Cite
[1]
R. Arisanti, R. S. Pontoh, S. Winarni, F. O. Wibowo, H. Khairunnisa, and R. A. Pratama, “ANALYTICAL APPROACH OF GENERALIZED LINEAR MODELS FOR HANDLING OVERDISPERSION IN POVERTY DATA OF INDONESIA”, BAREKENG: J. Math. & App., vol. 19, no. 3, pp. 1575-1586, Jul. 2025.
Issue
Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
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Articles

Copyright (c) 2025 Restu Arisanti, Resa Septiani Pontoh, Sri Winarni, Fellita Odelia Wibowo, Hanifah Khairunnisa, Raissheva Andika Pratama

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