A Short Note of Comparison between Convex and Non-convex Penalized Likelihood
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917935515172864 |
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| author | Du, Kasy |
| author_facet | Du, Kasy |
| contents | This paper compares convex and non-convex penalized likelihood methods in high-dimensional statistical modeling, focusing on their strengths and limitations. Convex penalties, like LASSO, offer computational efficiency and strong theoretical guarantees but often introduce bias in parameter estimation. Non-convex penalties, such as SCAD and MCP, reduce bias and achieve oracle properties but pose optimization challenges due to non-convexity. The paper highlights key differences in bias-variance trade-offs, computational complexity, and robustness, offering practical guidance for method selection. It concludes that the choice depends on the problem context, balancing accuracy |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07655 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Short Note of Comparison between Convex and Non-convex Penalized Likelihood Du, Kasy Methodology This paper compares convex and non-convex penalized likelihood methods in high-dimensional statistical modeling, focusing on their strengths and limitations. Convex penalties, like LASSO, offer computational efficiency and strong theoretical guarantees but often introduce bias in parameter estimation. Non-convex penalties, such as SCAD and MCP, reduce bias and achieve oracle properties but pose optimization challenges due to non-convexity. The paper highlights key differences in bias-variance trade-offs, computational complexity, and robustness, offering practical guidance for method selection. It concludes that the choice depends on the problem context, balancing accuracy |
| title | A Short Note of Comparison between Convex and Non-convex Penalized Likelihood |
| topic | Methodology |
| url | https://arxiv.org/abs/2502.07655 |