Hierarchically penalized Cox regression with grouped variables

• 成果类型: Article
• 署名作者: Wang, S.; Nan, B.; Zhu, N.; Zhu, J.
• 署名单位: University of Michigan System; University of Michigan
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asp016
• 发表日期: 2009
• 页码: 307322
• 关键词: model selection; adaptive lasso; breast-cancer; regularization; shrinkage

摘要

In many biological and other scientific applications, predictors are often naturally grouped. For example, in biological applications, assayed genes or proteins are grouped by biological roles or biological pathways. When studying the dependence of survival outcome on these grouped predictors, it is desirable to select variables at both the group level and the within-group level. In this article, we develop a new method to address the group variable selection problem in the Cox proportional hazards model. Our method not only effectively removes unimportant groups, but also maintains the flexibility of selecting variables within the identified groups. We also show that the new method offers the potential for achieving the asymptotic oracle property.