Nothing
enetLTS: Robust and Sparse Methods for High Dimensional Linear and Binary and Multinomial Regression
Fully robust versions of the elastic net estimator are introduced for linear and binary and multinomial regression, in particular high dimensional data. The algorithm searches for outlier free subsets on which the classical elastic net estimators can be applied. A reweighting step is added to improve the statistical efficiency of the proposed estimators. Selecting appropriate tuning parameters for elastic net penalties are done via cross-validation.
Getting started
Browse package contents
Package details |
|
|---|---|
| Author | Fatma Sevinc Kurnaz and Irene Hoffmann and Peter Filzmoser |
| Maintainer | Fatma Sevinc Kurnaz <fatmasevinckurnaz@gmail.com> |
| License | GPL (>= 3) |
| Version | 1.1.0 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
|
Try the enetLTS package in your browser
Any scripts or data that you put into this service are public.
