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arXiv · 1707.02331

Shrinkage Estimation Strategies in Generalized Ridge Regression Models Under Low/High-Dimension Regime

Bahadır Yüzbaşı·Mohammad Arashi·S. Ejaz Ahmed
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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.

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Bahadır Yüzbaşı, Mohammad Arashi, S. Ejaz Ahmed. 2017-07-07. Shrinkage Estimation Strategies in Generalized Ridge Regression Models Under Low/High-Dimension Regime. https://doi.org/10.1111/insr.12351

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