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Modified cross-validation for penalized high-dimensional linear regression models
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Yu, Yi and Feng, Yang (2014) Modified cross-validation for penalized high-dimensional linear regression models. Journal of Computational and Graphical Statistics, 23 (4). pp. 1009-1027. doi:10.1080/10618600.2013.849200 ISSN 1061-8600.
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Official URL: http://dx.doi.org/10.1080/10618600.2013.849200
Abstract
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. 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.
| Item Type: | Journal Article | ||||
|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
| Journal or Publication Title: | Journal of Computational and Graphical Statistics | ||||
| Publisher: | American Statistical Association | ||||
| ISSN: | 1061-8600 | ||||
| Official Date: | 20 October 2014 | ||||
| Dates: |
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| Volume: | 23 | ||||
| Number: | 4 | ||||
| Page Range: | pp. 1009-1027 | ||||
| DOI: | 10.1080/10618600.2013.849200 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Access rights to Published version: | Restricted or Subscription Access |
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