Guidelines for LASSO and derivatives use under different dependence and scale structures
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| Format: | Preprint |
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2025
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| _version_ | 1866916790121005056 |
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| author | Freijeiro-González, Laura Febrero-Bande, Manuel González-Manteiga, Wenceslao |
| author_facet | Freijeiro-González, Laura Febrero-Bande, Manuel González-Manteiga, Wenceslao |
| contents | In a multivariate linear regression model with $p>1$ covariates, implementation of penalization techniques often implies a preliminary univariate standardization step. Although this prevents scale effects on the covariates selection procedure, possible dependence structures can be disrupted, leading to wrong results. This is particularly challenging in high-dimensional settings where $p \geq n$. In this paper, we analyze the standardization effect on the LASSO for different dependence-scales contexts by means of an extensive simulation study. Two distinct objectives are pursued: adequate covariate selection and proper predictive capability. Additionally, its behavior is compared with the one of some well-known or innovative competitors. This comparison is also extended to three real datasets facing different dependence-scales patterns. Eventually, we conclude with discussion and guidelines on the most suitable methodology for each case in terms of covariates selection or prediction. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_08582 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Guidelines for LASSO and derivatives use under different dependence and scale structures Freijeiro-González, Laura Febrero-Bande, Manuel González-Manteiga, Wenceslao Methodology In a multivariate linear regression model with $p>1$ covariates, implementation of penalization techniques often implies a preliminary univariate standardization step. Although this prevents scale effects on the covariates selection procedure, possible dependence structures can be disrupted, leading to wrong results. This is particularly challenging in high-dimensional settings where $p \geq n$. In this paper, we analyze the standardization effect on the LASSO for different dependence-scales contexts by means of an extensive simulation study. Two distinct objectives are pursued: adequate covariate selection and proper predictive capability. Additionally, its behavior is compared with the one of some well-known or innovative competitors. This comparison is also extended to three real datasets facing different dependence-scales patterns. Eventually, we conclude with discussion and guidelines on the most suitable methodology for each case in terms of covariates selection or prediction. |
| title | Guidelines for LASSO and derivatives use under different dependence and scale structures |
| topic | Methodology |
| url | https://arxiv.org/abs/2506.08582 |