Penalized methods for bi-level variable selection.
Penalized methods for bi-level variable selection.
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DOI：
10.4310/sii.2009.v2.n3.a10
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发表时间：
2009-07-01
影响因子：
0.8
通讯作者：
Huang J
中科院分区：
文献类型：
--
作者：
Breheny P;Huang J
在许多应用中，协变量具有分组结构，该分组结构可以结合到分析中以选择重要的组以及这些组的重要成员。这项工作的重点是将分组结构纳入惩罚回归。我们研究了先前提出的组套索和组桥惩罚以及一种新的方法-组MCP，引入了一个框架并进行了仿真研究，揭示了这些方法的行为。为了适应这些模型，我们使用局部近似坐标下降的思想来开发算法，该算法即使在特征数量远远大于样本大小的情况下也是快速和稳定的。最后，这些方法被应用于年龄相关性黄斑变性的遗传关联研究。
In many applications, covariates possess a grouping structure that can be incorporated into the analysis to select important groups as well as important members of those groups. This work focuses on the incorporation of grouping structure into penalized regression. We investigate the previously proposed group lasso and group bridge penalties as well as a novel method, group MCP, introducing a framework and conducting simulation studies that shed light on the behavior of these methods. To fit these models, we use the idea of a locally approximated coordinate descent to develop algorithms which are fast and stable even when the number of features is much larger than the sample size. Finally, these methods are applied to a genetic association study of age-related macular degeneration.