Large-scale regression with non-convex loss and penalty

被引：16
|
作者：
Buccini, Alessandro [1 ]
Cabrera, Omar De la Cruz [2 ]
Donatelli, Marco [3 ]
Martinelli, Andrea [3 ]
Reichel, Lothar [2 ]
机构：
[1] Univ Cagliari, Dept Math & Comp Sci, I-09124 Cagliari, Italy
[2] Kent State Univ, Dept Math Sci, Kent, OH 44242 USA
[3] Univ Insubria, Dept Sci & High Technol, I-22100 Como, Italy
来源：
APPLIED NUMERICAL MATHEMATICS | 2020年 / 157卷
关键词：
Regression; Regularization; Robustness; Non-convex Optimization; MINIMIZATION; REGULARIZATION; SHRINKAGE; ALGORITHM; SPARSITY;
D O I：
10.1016/j.apnum.2020.07.006
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
We describe a computational method for parameter estimation in linear regression, that is capable of simultaneously producing sparse estimates and dealing with outliers and heavy-tailed error distributions. The method used is based on the image restoration method proposed in Huang et al. (2017) [13]. It can be applied to problems of arbitrary size. The choice of certain parameters is discussed. Results obtained for simulated and real data are presented. (C) 2020 IMACS. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码：590 / 601
页数：12
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