Univariate-Guided Sparse Regression
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866911020791889920 |
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| author | Chatterjee, Sourav Hastie, Trevor Tibshirani, Robert |
| author_facet | Chatterjee, Sourav Hastie, Trevor Tibshirani, Robert |
| contents | In this paper, we introduce ``UniLasso'' -- a novel statistical method for sparse regression. This two-stage approach preserves the signs of the univariate coefficients and leverages their magnitude. Both of these properties are attractive for stability and interpretation of the model. Through comprehensive simulations and applications to real-world datasets, we demonstrate that UniLasso outperforms Lasso in various settings, particularly in terms of sparsity and model interpretability. We prove asymptotic support recovery and mean-squared error consistency under a set of conditions different from the well-known irrepresentability conditions for the Lasso. Extensions to generalized linear models (GLMs) and Cox regression are also discussed. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_18360 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Univariate-Guided Sparse Regression Chatterjee, Sourav Hastie, Trevor Tibshirani, Robert Methodology 62J07 In this paper, we introduce ``UniLasso'' -- a novel statistical method for sparse regression. This two-stage approach preserves the signs of the univariate coefficients and leverages their magnitude. Both of these properties are attractive for stability and interpretation of the model. Through comprehensive simulations and applications to real-world datasets, we demonstrate that UniLasso outperforms Lasso in various settings, particularly in terms of sparsity and model interpretability. We prove asymptotic support recovery and mean-squared error consistency under a set of conditions different from the well-known irrepresentability conditions for the Lasso. Extensions to generalized linear models (GLMs) and Cox regression are also discussed. |
| title | Univariate-Guided Sparse Regression |
| topic | Methodology 62J07 |
| url | https://arxiv.org/abs/2501.18360 |