Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High‐Dimension Regime
本文针对多重共线性和高维问题，提出基于广义岭回归的预检验和收缩估计方法，通过模拟和实际数据比较其与惩罚方法的均方误差和预测误差，证明这些方法可作为正则化技术的有效替代。
Summary In this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high‐dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the shrinkage estimators to some penalty methods is compared and assessed by both simulation and real‐data analysis. We show that the suggested methods can be accounted as good competitors to regularisation techniques, by means of a mean squared error of estimation and prediction error. A thorough comparison of pretest and shrinkage estimators based on the maximum likelihood method to the penalty methods. In this paper, we extend the comparison outlined in his work using the least squares method for the generalised ridge regression.