Modified ridge‐type estimator to combat multicollinearity: Application to chemical data
By Adewale F. Lukman, Kayode Ayinde, S. Binuomote, Onate A. Clement. Journal of Chemometrics, 2019
145 citations recorded by the source corpus.
The Linear regression model is one of the most widely used models in different fields of study. The most popularly used estimation technique is the ordinary least squares estimator. The technique becomes unstable and gives misleading result in the presence of multicollinearity. The ridge regression estimator has been widely accepted as an alternative method to combat the problem of multicollinearity. In this study, a modified ridge‐type estimator is suggested by modifying the ridge regression estimator. A Monte Carlo simulation study and real‐life application were conducted to compare the performance of this estimator and some other existing estimators. The results of both simulation study and real‐life application show that the proposed estimator outperforms other competing estimator.
Subfields: Analytical Chemistry, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/4d467287866f3a9b6289e340c78490a4ad42f3eb
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In Analytical Chemistry
In Statistics and Probability
- Issam Dawoud
- B. M. G. Kibria
- Kristofer Månsson
- Ghazi Shukur
- Zakariya Yahya Algamal
- B. G. Golam Kibria
- Yazid M. Al-Hassan
- A. V. Dorugade
Citations in the corpus
- A New Biased Estimator to Combat the Multicollinearity of the Gaussian Linear Regression Model (2020, background): “[13], and, recently, Kibria and Lukman [15], among others.”