Optimum Ridge Regression Parameter Using R-Squared of Prediction as a Criterion for Regression Analysis
- https://doi.org/10.2991/jsta.d.210322.001How to use a DOI?
- Ridge parameter; PRESS; Maximization of R2-prediction; Model prediction power
The presence of the multicollinearity problem in the predictor data causes the variance of the ordinary linear regression coefficients to be increased so that the prediction power of the model not to be satisfied and sometimes unacceptable results be predicted. The ridge regression has been proposed as an efficient method to combat multicollinearity problem long ago. In application of ridge regression the researcher uses the ridge trace and selects a value of ridge parameter in such a manner that he thinks the regression coefficients have stabilized; this leads the ridge regression to be subjective technique. The purpose of this paper is the conversion of the ridge regression method from a qualitative method to a quantitative one meanwhile to present a method to find the optimum ridge regression parameter which maximizes the R-squared of prediction. We examined four well-known case studies on this regard. Significant improvements at all of the cases demonstrated the validity of our proposed method.
- © 2021 The Authors. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Akbar Irandoukht PY - 2021 DA - 2021/03/26 TI - Optimum Ridge Regression Parameter Using R-Squared of Prediction as a Criterion for Regression Analysis JO - Journal of Statistical Theory and Applications SP - 242 EP - 250 VL - 20 IS - 2 SN - 2214-1766 UR - https://doi.org/10.2991/jsta.d.210322.001 DO - https://doi.org/10.2991/jsta.d.210322.001 ID - Irandoukht2021 ER -