sparsegl: An R Package for Estimating Sparse Group Lasso
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916557063454720 |
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| author | Liang, Xiaoxuan Cohen, Aaron Heinsfeld, Anibal Solón Pestilli, Franco McDonald, Daniel J. |
| author_facet | Liang, Xiaoxuan Cohen, Aaron Heinsfeld, Anibal Solón Pestilli, Franco McDonald, Daniel J. |
| contents | The sparse group lasso is a high-dimensional regression technique that is useful for problems whose predictors have a naturally grouped structure and where sparsity is encouraged at both the group and individual predictor level. In this paper we discuss a new R package for computing such regularized models. The intention is to provide highly optimized solution routines enabling analysis of very large datasets, especially in the context of sparse design matrices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2208_02942 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | sparsegl: An R Package for Estimating Sparse Group Lasso Liang, Xiaoxuan Cohen, Aaron Heinsfeld, Anibal Solón Pestilli, Franco McDonald, Daniel J. Methodology The sparse group lasso is a high-dimensional regression technique that is useful for problems whose predictors have a naturally grouped structure and where sparsity is encouraged at both the group and individual predictor level. In this paper we discuss a new R package for computing such regularized models. The intention is to provide highly optimized solution routines enabling analysis of very large datasets, especially in the context of sparse design matrices. |
| title | sparsegl: An R Package for Estimating Sparse Group Lasso |
| topic | Methodology |
| url | https://arxiv.org/abs/2208.02942 |