Journal article
A Selective Review of Group Selection in High-Dimensional Models
Statistical science, Vol.27(4), pp.481-499
2012
DOI: 10.1214/12-STS392
PMCID: PMC3810358
PMID: 24174707
Abstract
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.
Details
- Title: Subtitle
- A Selective Review of Group Selection in High-Dimensional Models
- Creators
- Jian Huang - Department of Statistics and Actuarial Science, 241 SH, University of Iowa, Iowa City, Iowa 52242, USAPatrick Breheny - Department of Statistics, University of Kentucky, Lexington, Kentucky 40506, USAShuangge Ma - Division of Biostatistics, School of Public Health, Yale University, New Haven, Connecticut 06520, USA
- Resource Type
- Journal article
- Publication Details
- Statistical science, Vol.27(4), pp.481-499
- DOI
- 10.1214/12-STS392
- PMID
- 24174707
- PMCID
- PMC3810358
- NLM abbreviation
- Stat Sci
- ISSN
- 0883-4237
- eISSN
- 2168-8745
- Grant note
- R01 CA120988 || CA / National Cancer Institute : NCI R01 CA142774 || CA / National Cancer Institute : NCI
- Language
- English
- Date published
- 2012
- Academic Unit
- Statistics and Actuarial Science; Biostatistics
- Record Identifier
- 9983997486602771
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