Data · dataset · 2014
Modified Cross-Validation for Penalized High-Dimensional Linear Regression Models
Listed in DataCite
In this article, for Lasso penalized linear regression models in high-dimensional settings, we propose a modified cross-validation (CV) method for selecting the penalty parameter.
Description
The methodology is extended to other penalties, such as Elastic Net. We conduct extensive simulation studies and real data analysis to compare the performance of the modified CV method with other methods.
It is shown that the popular K -fold CV method includes many noise variables in the selected model, while the modified CV works well in a wide range of coefficient and correlation settings. Supplementary materials containing the computer code are available online.
Links
Where it is published
- Repository landing page tandf.figshare.com/articles/dataset/Modified_Cross_Validation_for_Penalized_High_… ↗
landing page · from DataCite
- DOI doi.org/10.6084/m9.figshare.1209695.v1 ↗
DOI / persistent id · from DataCite
Documentation and papers
- Creative Commons Attribution 4.0 International creativecommons.org/licenses/by/4.0/legalcode ↗
license · from DataCite
- IsSupplementTo 10.1080/10618600.2013.849200 doi.org/10.1080/10618600.2013.849200 ↗
publication · from DataCite
Catalogue records · 2
- DataCite API api.datacite.org/dois/10.6084/m9.figshare.1209695.v1 ↗
metadata API · from DataCite
- DataCite Commons commons.datacite.org/doi.org/10.6084/m9.figshare.1209695.v1 ↗
catalogue entry · from DataCite
Topics
- Stated by source
- Biological sciences · Mathematics
- From keywords
- Chemistry · Life Sciences · Mathematics & Statistics
- Inferred from text
- Simulation 75%
Related
Provenance · 1 source records, 14 field assertions
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|---|---|---|---|
| DataCite | 10.6084/m9.figshare.1209695.v1 | 13 d ago | JSON v1 |
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| concepts[field].local:field:chemistry | mapping · DataCite | vocabulary-mapper@1.0.0 | keywords['Chemistry'] |
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