ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1509.02957
  4. Cited By
Semismooth Newton Coordinate Descent Algorithm for Elastic-Net Penalized
  Huber Loss Regression and Quantile Regression

Semismooth Newton Coordinate Descent Algorithm for Elastic-Net Penalized Huber Loss Regression and Quantile Regression

9 September 2015
Congrui Yi
Jian Huang
ArXivPDFHTML

Papers citing "Semismooth Newton Coordinate Descent Algorithm for Elastic-Net Penalized Huber Loss Regression and Quantile Regression"

3 / 3 papers shown
Title
Support estimation in high-dimensional heteroscedastic mean regression
Support estimation in high-dimensional heteroscedastic mean regression
P. Hermann
H. Holzmann
110
0
0
03 Nov 2020
Strong rules for discarding predictors in lasso-type problems
Strong rules for discarding predictors in lasso-type problems
Robert Tibshirani
Jacob Bien
J. Friedman
Trevor Hastie
N. Simon
Jonathan E. Taylor
Robert Tibshirani
180
640
0
09 Nov 2010
Pathwise coordinate optimization
Pathwise coordinate optimization
J. Friedman
Trevor Hastie
Holger Hofling
Robert Tibshirani
258
2,053
0
10 Aug 2007
1