A Comparison between Some Penalized Methods for Estimating Parameters

Abstract

Regression analysis is one of the most popular statistical methods in various biological and economic studies where, frequently, the number of explanatory variables becomes large. Penalized methods have been adapted and have gained popularity as a key for simultaneously performing variable selection and model estimation. This paper proposes contamination procedure from the viewpoint of different types of penalized regression, aiming at identifying any types of penalized methods that are best to deal with contamination data. This paper demonstrates that the Lasso regression is the best method for contamination data depending on the heavy tail distribution behavior of the response variables and using simulation for (15%) data with contamination. The comparison between types of penalized methods based on the statistical criterion (MAE and MSE) and results shows that the Lasso regression is better than another type of penalized method