A Selective Review of Group Selection in High-Dimensional Models.
A Selective Review of Group Selection in High-Dimensional Models.
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DOI：
10.1214/12-sts392
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Ma S
中科院分区：
文献类型：
--
作者：
Huang J;Breheny P;Ma S
分组结构在许多统计建模问题中自然出现。考虑到变量的分组结构，提出了几种选择变量的方法。例子包括群LASSO和几种凹群选择方法。在这篇文章中，我们给出了关于群体选择的方法发展，理论性质和计算算法的选择性回顾。我们特别关注涉及凹惩罚的群体选择方法。我们讨论了群体选择和双层次选择方法。我们描述了这些方法在非参数加性模型、半参数回归、看似不相关的回归、基因组数据分析和全基因组关联研究中的几种应用。我们还强调了一些需要进一步研究的问题。
Grouping structures arise naturally in many statistical modeling problems. Several methods have been proposed for variable selection that respect grouping structure in variables. Examples include the group LASSO and several concave group selection methods. In this article, we give a selective review of group selection concerning methodological developments, theoretical properties and computational algorithms. We pay particular attention to group selection methods involving concave penalties. We address both group selection and bi-level selection methods. We describe several applications of these methods in nonparametric additive models, semiparametric regression, seemingly unrelated regressions, genomic data analysis and genome wide association studies. We also highlight some issues that require further study.