According to simulation study, our estimators perform better than the others in most of the situations in the sense of MSE.
📌 3 Key Takeaways
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Objective & Hypothesis:In multiple regression analysis, the independent variables should be uncorrelated within each other.
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Key Findings & Data:If they are highly intercorrelated, this serious problem is called multicollinearity.
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Conclusion & Impact:According to simulation study, our estimators perform better than the others in most of the situations in the sense of MSE.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Multicollinearity · multiple linear regression · ridge regression
Abstract
In multiple regression analysis, the independent variables should be uncorrelated within each other. If they are highly intercorrelated, this serious problem is called multicollinearity. There are several methods to get rid of this problem and one of the most famous one is the ridge regression. In this paper, we will propose some modified ridge parameters. We will compare our estimators with some estimators proposed earlier according to mean squared error (MSE) criterion. All results are calculated by a Monte Carlo simulation. According to simulation study, our estimators perform better than the others in most of the situations in the sense of MSE.
ASAR, Y., KARAİBRAHİMOĞLU, A., GENÇ, A. (2022). Modified ridge regression parameters: A comparative Monte Carlo study. Hacettepe Journal of Mathematics and Statistics, Cilt: 43 - Sayı: 5, 827–841. https://ajindex.com/articles/530326