Smoothing ADMM for Sparse-Penalized Quantile Regression With Non-Convex Penalties
This paper investigates quantile regression in the presence of non-convex and non-smooth sparse penalties, such as the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD). The non-smooth and non-convex nature of these problems often leads to convergence difficulties for many...
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| Autori principali: | , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
IEEE
2024-01-01
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| Serie: | IEEE Open Journal of Signal Processing |
| Soggetti: | |
| Accesso online: | https://ieeexplore.ieee.org/document/10365338/ |
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