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...
Guardat en:
| Autors principals: | , , , |
|---|---|
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
IEEE
2024-01-01
|
| Col·lecció: | IEEE Open Journal of Signal Processing |
| Matèries: | |
| Accés en línia: | https://ieeexplore.ieee.org/document/10365338/ |
| Etiquetes: |
Sense etiquetes, Sigues el primer a etiquetar aquest registre!
|