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Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent
Open accessAbstract
We introduce a pathwise algorithm for the Cox proportional hazards model, regularized by convex combinations of l1 and l2 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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“We use the glmnet package in R for computation (Simon et al., 2011), where the coefficients are estimated by cyclical coordinate descent (Friedman et al., 2010).”
Method
Metadata
- Authors
- Simon, N., Friedman, J., Hastie, T., & Tibshirani, R.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2011
- Volume
- 39
- Issue
- 5
- Pages
- 1-13
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W2157076315
- Cited by
- 2,499
- References
- 1
- FWCI
- 22.26
- Type
- journal article
- Language
- en
- Open access
- diamond
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.