Modified Cross-Validation for Penalized High-Dimensional Linear Regression Models

Yi Yu, Yang Feng

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)1009-1027
Number of pages19
JournalJournal of Computational and Graphical Statistics
Volume23
Issue number4
DOIs
StatePublished - Oct 25 2014

Keywords

  • Lasso
  • Tuning parameter selection

ASJC Scopus subject areas

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty

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