Journal article
Penalized methods for bi-level variable selection
Statistics and its interface, Vol.2(3), pp.369-380
2009
DOI: 10.4310/SII.2009.v2.n3.a10
PMCID: PMC2904563
PMID: 20640242
Abstract
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.
Details
- Title: Subtitle
- Penalized methods for bi-level variable selection
- Creators
- Patrick BrehenyJian Huang
- Resource Type
- Journal article
- Publication Details
- Statistics and its interface, Vol.2(3), pp.369-380
- DOI
- 10.4310/SII.2009.v2.n3.a10
- PMID
- 20640242
- PMCID
- PMC2904563
- ISSN
- 1938-7997
- eISSN
- 1938-7997
- Language
- English
- Date published
- 2009
- Academic Unit
- Statistics and Actuarial Science; Biostatistics
- Record Identifier
- 9983985928302771
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