Journal
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
Volume 23, Issue 4, Pages 1009-1027Publisher
AMER STATISTICAL ASSOC
DOI: 10.1080/10618600.2013.849200
Keywords
Lasso; Tuning parameter selection
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Funding
- NSF [DMS-1308566]
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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.
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