Smoothing ADMM for Sparse-Penalized Quantile Regression With Non-Convex Penalties

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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Bibliographic Details
Main Authors: Reza Mirzaeifard, Naveen K. D. Venkategowda, Vinay Chakravarthi Gogineni, Stefan Werner
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Open Journal of Signal Processing
Subjects:
Quantile regression
non-smooth and non-convex penalties
ADMM
sparse learning
Online Access:https://ieeexplore.ieee.org/document/10365338/

Internet

https://ieeexplore.ieee.org/document/10365338/

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