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Robust estimation methods for addressing multicollinearity and outliers in beta regression models.
Olalekan T Olaluwoye1, Adewale F Lukman2, Masad A Alrasheedi3
1African Institute for Mathematical Sciences (AIMS), Mbour, Senegal.
Scientific Reports
|April 4, 2025
Summary
This study introduces robust beta regression estimators to combat multicollinearity and outliers. The proposed Logit Surrogate Maximum Likelihood Estimator (BR-LSMLE) shows improved reliability for [0, 1] interval data.
Area of Science:
- Statistical modeling
- Econometrics
- Data analysis
Background:
- Beta regression is crucial for [0, 1] interval data in sciences.
- Multicollinearity and outliers challenge traditional maximum likelihood estimators (MLE).
Purpose of the Study:
- To develop robust beta regression estimators mitigating multicollinearity and outlier effects.
- To enhance the reliability of beta regression models in empirical research.
Main Methods:
- Combining ridge estimation with robust beta estimators.
- Evaluating performance via simulation and real-world data (gasoline yield, firm cost, education).
Main Results:
- Proposed robust estimators show greater resilience to outliers and multicollinearity than standard MLE.
- The Logit Surrogate Maximum Likelihood Estimator (BR-LSMLE) demonstrated superior performance.
Conclusions:
- Robust estimation techniques are vital for accurate beta regression.
- BR-LSMLE is a suitable alternative for datasets with multicollinearity and outliers.
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