General Letters in Mathematics

Volume 12 - Issue 3 (3) | PP: 126 - 138 Language : English
DOI : https://doi.org/10.31559/glm2022.12.3.3
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Comparison of Some Group Variable Selection Methods in High Dimensional Multiple Linear Regression via Simulation Study

Rejin Abdulqader Ibrahim ,
Hussein Abdulrahman Hashem
Received Date Revised Date Accepted Date Publication Date
27/7/2022 3/9/2022 22/9/2022 17/10/2022
Abstract
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.


How To Cite This Article
Ibrahim , R. A. & Hashem , H. A. (2022). Comparison of Some Group Variable Selection Methods in High Dimensional Multiple Linear Regression via Simulation Study. General Letters in Mathematics, 12 (3), 126-138, 10.31559/glm2022.12.3.3

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