Lukman, A. F. and Ayinde, K. and Binuomote, S. and Onate, C.A (2019) Modified ridge‐type estimator to combat multicollinearity: Application to chemical data. Journal of Chemometrics. (In Press)
Full text not available from this repository.Abstract
The Linear regression model is one of the most widely used models in differentfields of study. The most popularly used estimation technique is the ordinaryleast squares estimator. The technique becomes unstable and gives misleadingresult in the presence of multicollinearity. The ridge regression estimator hasbeen widely accepted as an alternative method to combat the problem ofmulticollinearity. In this study, a modified ridge‐type estimator is suggestedby modifying the ridge regression estimator. A Monte Carlo simulation studyand real‐life application were conducted to compare the performance of thisestimator and some other existing estimators. The results of both simulationstudy and real‐life application show that the proposed estimator outperformsother competing estimator.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > QD Chemistry |
| Depositing User: | Mr DIGITAL CONTENT CREATOR LMU |
| Date Deposited: | 07 May 2019 10:11 |
| Last Modified: | 18 Sep 2019 11:37 |
| URI: | https://eprints.lmu.edu.ng/id/eprint/2144 |
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