Shrinkage parameter selection via modified cross-validation approach for ridge regression model

Abstract

The ridge regression estimator has been consistently demonstrated to be an attractive shrinkage method to reduce the effects of multicollinearity. The choice of the ridge shrinkage parameter is critical. Cross-validation method is a widely adopted method for shrinkage parameter selection. However, cross-validation method suffers from instability in determining the best shrinkage parameter. To address this problem, a modification of the cross-validation method is proposed by repeating fold assignment. And then, a proper quantile value of the best shrinkage parameter values is utilized. Simulation and real data example results demonstrate that the proposed method is outperformed cross-validation and generalized cross-validation methods.