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

By Patrick Breheny, Jian Huang. Statistics and computing, 2012

324 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b8d527eea7a492d8673d1b551208f0b0b6198182

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Citations in the corpus

High dimensional ordinary least squares projection for screening variables (2015, methodology): “As reported in Breheny and Huang (2013), this gene is also selected via group Lasso and group SCAD.”