title: Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent.
abstract: We introduce a pathwise algorithm for the Cox proportional hazards model, regularized by convex combinations of ℓ1and ℓ2penalties (elastic net). Our algorithm fits via cyclical coordinate descent, and employs warm starts to find a solution along a regularization path. We demonstrate the efficacy of our algorithm on real and simulated data sets, and find considerable speedup between our algorithm and competing methods.
authors.affiliation: Department of Statistics, Stanford University, 390 Serra Mall, Stanford CA, 94305, United States of America.
authors.forename: Noah
authors.initials: N
authors.lastname: Simon
authors.affiliation: Department of Statistics, Stanford University, 390 Serra Mall, Stanford CA, 94305, United States of America.
authors.forename: Jerome
authors.initials: J
authors.lastname: Friedman
authors.affiliation: Department of Statistics, Stanford University, 390 Serra Mall, Stanford CA, 94305, United States of America.
authors.forename: Trevor
authors.initials: T
authors.lastname: Hastie
authors.affiliation: Department of Statistics, Stanford University, 390 Serra Mall, Stanford CA, 94305, United States of America.
authors.forename: Rob
authors.initials: R
authors.lastname: Tibshirani
keywords: Cox model; elastic net; lasso; survival