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All Journal Jurnal TIMES Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam Jurnal Statistika dan Aplikasinya MATH LOCUS: Jurnal Riset dan Inovasi Pendidikan Matematika MATHunesa: Jurnal Ilmiah Matematika Advance Sustainable Science, Engineering and Technology (ASSET) Indonesian Journal of Statistics and Its Applications Limits: Journal of Mathematics and Its Applications
Erfiani
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Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Physics Social Sciences Other

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Journal : Advance Sustainable Science, Engineering and Technology (ASSET)

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Comparative Performance of GLMM and GEE for Longitudinal Beta Regression in Economic Inequality Modelling Sihombing, Pardomuan Robinson; Erfiani; Khairil Anwar Notodiputro; Anang Kurnia
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.2057

Abstract

Due to the shortcomings of conventional Gaussian methods, specialized models are frequently needed for longitudinal data analysis with bounded outcomes, such as the Gini ratio. In order to model economic inequality in Indonesia, this study compares the effectiveness of Generalized Linear Mixed Models (GLMM) and Generalized Estimating Equations (GEE) for beta-distributed longitudinal data. Root Mean Square Error (RMSE) and pseudo R-squared values are used to assess model performance using panel data from 10 provinces between 2018 and 2024 as well as important socioeconomic indicators. With lower RMSE and higher explanatory power across all provincial subsets, the results consistently demonstrate that GLMM performs better than both GEE and generalized linear models (GLM). ANOVA tests verify that modeling methodologies, not data heterogeneity in GRDP or Gini values, are responsible for the differences in model performance. These results demonstrate how well GLMM handles complex data structures and within-subject correlations, providing more accurate and effective estimates in longitudinal beta regression scenarios. The study encourages the use of GLMM for more precise longitudinal analysis in economic and social research and offers insightful information for researchers modeling inequality indices.
Co-Authors Agus Mohamad Soleh Alfa Nugraha ALIU, MUFTIH ALWI Amanda, Nabila Amelia, Reni Anang Kurnia ASEP SAEFUDDIN Fitrianto, Anwar Gustiara, Dela I Made Sumertajaya Indahwati Irsyifa Mayzela Afnan Jumansyah, L.M. Risman Dwi L.M. Risman Dwi Jumansyah M. Yunus Merryanty Lestari P Muftih Alwi Aliu Nur Hidayah Pardomuan Robinson Sihombing Pika Silvianti Rifda Nida’ul Labibah Riska Yulianti, Riska Rizki, Akbar Setyowati, Silfiana Lis Siti Hafsah Siti Hasanah Siti Nur Azizah, Siti Nur Surya, Ardi Waliulu, Megawati Zein