Abstract:

Penalized regression methods are often used to obtain stable coefficient estimates in case of multicollinearity problems in the dataset. In addition, these methods can make automatic variable selection depending on the nature of the penalty term applied. In this study, a detailed comparison of the performances of ridge, LASSO, elastic net and adaptive LASSO penalized regression methods, which are widely used in the literature, is made through simulation studies depending on the structure of the real coefficient vector. Mean squared error on the test set, misclassification rate, false positive rate and active set sizes are used as comparison criteria in the study. Simulation studies show that the structure of the real coefficient vector has a significant effect on the model performance revealed by the methods. 

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