Journal
JOURNAL OF STATISTICAL SOFTWARE
Volume 33, Issue 1, Pages 1-22Publisher
JOURNAL STATISTICAL SOFTWARE
DOI: 10.18637/jss.v033.i01
Keywords
lasso; elastic net; logistic regression; l(1) penalty; regularization path; coordinate-descent
Funding
- National Science Foundation [DMS-97-64431, DMS-0505676, DMS-9971405]
- National Institutes of Health [2R01 CA 72028-07, N01-HV-28183]
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We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multinomial regression problems while the penalties include l(1) (the lasso), l(2) (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.
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