Comparison of GEE and GLMM Methods for Longitudinal Data (Case Study: Determinants of the Percentage of Poor People in Indonesia, 2015-2019)

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Abstract

The development model of the GLM for longitudinal data that has not normally distributed (but still in the exponential family) and correlates with response variables is the Generalized Estimating Equations (GEE) and Generalized Linear Mixed-effects Model (GLMM) models. This study compares the GEE model with the GLMM on longitudinal data in modeling poor people in Indonesia in 2015-2019. The data source used is from the publication of the Central Statistics Agency. Based on the smaller RMSE and AIC criteria, the GLMM model is better than the GEE model in modeling the percentage of poor people in Indonesia. The Gini ratio, the rate of Households in Slums, and the percentage of Informal Workers have a significant positive effect on the percentage of poor people. Meanwhile, the percentage of households having access to HDI, economic growth, domestic and foreign investment value have a significant negative impact on poor people.

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APA

Sihombing, P. R., Notodiputro, K. A., & Sartono, B. (2022). Comparison of GEE and GLMM Methods for Longitudinal Data (Case Study: Determinants of the Percentage of Poor People in Indonesia, 2015-2019). In AIP Conference Proceedings (Vol. 2563). American Institute of Physics Inc. https://doi.org/10.1063/5.0103254

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