Group Variable Selection Methods with Quantile Regression: A Simulation Study.
In many cases, covariates have a grouping structure that can be used in the analysis to identify important groups and the significant members of those groups. This paper reviews some group variable selection methods that utilize quantile regression. The study compares seven previously proposed group...
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| Main Author: | |
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| Format: | Article |
| Language: | Arabic |
| Published: |
College of Computer Science and Mathematics, University of Mosul
2025-06-01
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| Series: | المجلة العراقية للعلوم الاحصائية |
| Subjects: | |
| Online Access: | https://stats.uomosul.edu.iq/article_187759_4911919339b73a13b131ebcd6427170e.pdf |
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| Summary: | In many cases, covariates have a grouping structure that can be used in the analysis to identify important groups and the significant members of those groups. This paper reviews some group variable selection methods that utilize quantile regression. The study compares seven previously proposed group variable selection methods, namely the group Lasso estimate, the quantile group Lasso (median group Lasso) estimate, the quantile group adaptive Lasso estimate, the sparse group Lasso estimate, the group scad estimate, the group mcp estimate, and the group gel estimate through a simulation study. The simulation study helps determine which methods perform best in all linear regression scenarios. |
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| ISSN: | 1680-855X 2664-2956 |