Journal article
Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors
Statistics and Computing, Vol.25(2), pp.173-187
03/2015
DOI: 10.1007/s11222-013-9424-2
PMCID: PMC4349417
PMID: 25750488
Abstract
Penalized regression is an attractive framework for variable selection problems. Often, variables possess a grouping structure, and the relevant selection problem is that of selecting groups, not individual variables. The group lasso has been proposed as a way of extending the ideas of the lasso to the problem of group selection. Nonconvex penalties such as SCAD and MCP have been proposed and shown to have several advantages over the lasso; these penalties may also be extended to the group selection problem, giving rise to group SCAD and group MCP methods. Here, we describe algorithms for fitting these models stably and efficiently. In addition, we present simulation results and real data examples comparing and contrasting the statistical properties of these methods.
Details
- Title: Subtitle
- Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors
- Creators
- Patrick Breheny - Department of Biostatistics University of Iowa Iowa City USJian Huang - Department of Statistics and Actuarial Sciences University of Iowa Iowa City US
- Resource Type
- Journal article
- Publication Details
- Statistics and Computing, Vol.25(2), pp.173-187
- DOI
- 10.1007/s11222-013-9424-2
- PMID
- 25750488
- PMCID
- PMC4349417
- NLM abbreviation
- Stat Comput
- ISSN
- 0960-3174
- eISSN
- 1573-1375
- Publisher
- Springer US; Boston
- Language
- English
- Date published
- 03/2015
- Academic Unit
- Statistics and Actuarial Science; Biostatistics
- Record Identifier
- 9983985706702771
Metrics
30 Record Views