.png){kind=icon}
.png){kind=icon}
Return
Robust and sparse estimation methods for high-dimensional linear and logistic regression
.png){kind=icon}
OA
AI
DOI：10.1016/j.chemolab.2017.11.017.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Fully robust versions of the elastic net estimator are introduced for linear and logistic regression. The algorithms used to compute the estimators are based on the idea of repeatedly applying the non-robust classical estimators to data subsets only. It is shown how outlier-free subsets can be identified efficiently, and how appropriate tuning parameters for the elastic net penalties can be selected. A final reweighting step improves the efficiency of the estimators. Simulation studies compare with non-robust and other competing robust estimators and reveal the superiority of the newly proposed methods. This is also supported by a reasonable computation time and by good performance in real data examples.
Keywords:
Elastic net penalty
Least trimmed squares
C-step algorithm
High-dimensional data
Robustness
Sparse estimation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.8
Papers:
4.6K
Citations:
1.2W