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Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent

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Author Info

  • Noah Simon
  • Jerome H. Friedman
  • Trevor Hastie
  • Rob Tibshirani
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    Abstract

    We introduce a pathwise algorithm for the Cox proportional hazards model, regularized by convex combinations of l_1 and l_2 penalties (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.

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    File URL: http://www.jstatsoft.org/v39/i05/paper
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    Bibliographic Info

    Article provided by American Statistical Association in its journal Journal of Statistical Software.

    Volume (Year): 39 ()
    Issue (Month): i05 ()
    Pages:

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    Handle: RePEc:jss:jstsof:39:i05

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    Web page: http://www.jstatsoft.org/

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    Cited by:
    1. Biagini, Francesca & Groll, Andreas & Widenmann, Jan, 2013. "Intensity-based premium evaluation for unemployment insurance products," Insurance: Mathematics and Economics, Elsevier, vol. 53(1), pages 302-316.
    2. Lian, Heng & Li, Jianbo & Tang, Xingyu, 2014. "SCAD-penalized regression in additive partially linear proportional hazards models with an ultra-high-dimensional linear part," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 50-64.

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