Application of The Inverse Gaussian Hybrid Estimator (Igh) To Address Multicollinearity in The Number of Tuberculosis Cases
DOI:
https://doi.org/10.59890/ijsas.v4i4.411Keywords:
Inverse Gaussian Regression, IGML, IGH, Multicolinearitas, Tuberculosis, Mean Square Error.Abstract
The Inverse Gaussian Regression (IGR) model is one approach within the Generalized Linear Model (GLM) framework for modeling data with a positively skewed distribution. Parameter estimation is typically performed using the Inverse Gaussian Maximum Likelihood (IGML) method. However, under conditions of high multicollinearity, IGML becomes unstable due to increased coefficient variance, which leads to a higher MSE. This study compares IGML with the Inverse Gaussian Hybrid Estimator (IGH) in addressing multicollinearity in Tuberculosis cases across 28 districts/cities in West Java Province from 2022-2024. The analysis results indicate the presence of multicollinearity, characterized by high correlation values and large VIF values. The IGH method produces coefficient shrinkage, making the model more stable and superior to IGML.
References
Amin, M., Lukman, A. F., Ayinde, K., & Ogundimu, E. O. (2020). On the performance of some biased estimators in inverse Gaussian regression model. Journal of Statistical Computation and Simulation, 90(12), 2141–2160.
Badan Pusat Statistik Provinsi Jawa Barat. (2024). Statistik kesehatan Jawa Barat 2024.
Fisher, R. A. (1992). Regression diagnostics: Identifying influential data and sources of collinearity. John Wiley & Sons.
Greene, W. H. (2018). Basic econometrics (5th ed.). McGraw-Hill International Edition.
Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics (5th ed.). McGraw-Hill International Edition.
Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55–67.
Jong, P. de, & Heller, G. Z. (2008). Generalized linear models for insurance data. Cambridge University Press.
Kibria, B. M. G. (2003). Performance of some new ridge regression estimators. Communications in Statistics – Simulation and Computation, 32(2), 419–435.
McCullagh, P., & Nelder, J. A. (1989). Generalized linear models (2nd ed.). Chapman & Hall.
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis (6th ed.). John Wiley & Sons.
Schrödinger, E. (1915). Zur Theorie der Fall- und Steigversuche an Teilchen mit Brownscher Bewegung. Physikalische Zeitschrift, 16, 289–295.
World Health Organization. (2024). Global tuberculosis report 2024.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Gracia Trifena Sintauli, Netti Herawati, Misgiyati, Nusyirwan
This work is licensed under a Creative Commons Attribution 4.0 International License.
