Comparison of some group variable selection methods in high dimensional multiple linear regression via simulation study

Abstract EN

In many applications, covariates possess a grouping structure that can be incorporated into the analysis to select important groups as well as important members of those groups.

In this paper, we reviewed some group variable selection methods in the penalized regression model.

This paper investigates by comparing the performance of seven previously proposed group variable selection methods; the group Lasso estimates, the group Lasso net estimates, the sparse group Lasso estimates, the group scad estimates, the group scad net estimates, the group mcp estimates, and the group gel estimate via a simulation study.

The simulation study is used in determining which methods are best in all of the linear regression scenarios.