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2015
ACM

Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors

3 years 5 months ago
Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors
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.
Patrick Breheny, Jian Huang
Added 17 Apr 2016
Updated 17 Apr 2016
Type Journal
Year 2015
Where SAC
Authors Patrick Breheny, Jian Huang
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