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arXiv · 2506.08582

Guidelines for LASSO and derivatives use under different dependence and scale structures

Laura Freijeiro-González·Manuel Febrero-Bande·Wenceslao González-Manteiga
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Abstract

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.

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Laura Freijeiro-González, Manuel Febrero-Bande, Wenceslao González-Manteiga. 2025-06-10. Guidelines for LASSO and derivatives use under different dependence and scale structures. https://arxiv.org/abs/2506.08582

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