Comparative study of L1 regularized logistic regression methods for variable selection

L-1 regularized logistic regression consists an important tool in data science and is dedicated to solve sparse generalized linear problems. The L-1 regularization is widely used in variable selection and estimation in generalized linear model analysis. This approach is intended to select the statistically important predictors. In this paper we compare the performance of some existing L-1 regularized logistic regression methods. The goal of our simulation study is directed toward the variable selection performance of regularized logistic regression in high dimensions. We consider three varying n (number of observations), p (number of predictors) settings and we support this comparison analysis by conducting various simulated experiments taking into consideration the correlation structure of the design matrix.