Title: Shrinkage Estimation Strategies in Generalized Ridge Regression Models Under Low/High-Dimension Regime,
Abstract: In this study, we propose shrinkage methods based on {\it generalized ridge
regression} (GRR) estimation which is suitable for both multicollinearity and
high dimensional problems with small number of samples (large $p$, small $n$).
Also, it is obtained theoretical properties of the proposed estimators for
Low/High Dimensional cases. Furthermore, the performance of the listed
estimators is demonstrated by both simulation studies and real-data analysis,
and compare its performance with existing penalty methods. We show that the
proposed methods compare well to competing regularization techniques.