COMPARISON BETWEEN PROPOSED METHOD FOR WAVELET LASSO AND CLASSICAL PENALIZED METHOD (SIMULATION STUDY)

Abstract

We propose an unused strategy for estimation in direct models. The 'Wavelet Lasso' minimizes the remaining sum of squares subject to the entirety of the supreme esteem of the coefficients being less than consistent. Since of the nature of this limitation, it tends to create a few coefficients that are preciseness and consequently give interpretable models.

This paper proposes a simple estimate for tuning parameters based on wavelet shrinkage of penalized method (Lasso) compared with the classic penalized method depending on the tail probability behavior of the response variables and using simulation experiments for (10% and 50%) contamination and real data. The comparing results between the proposed method with a classic penalized method based on the statistical criterion (MAE and MSE). It was concluded that the wavelet shrinkage of penalized method gives the best results and a more accurate classical method for all simulations and real data based on (MAE and MSE) criteria.