Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent.
Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent.
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DOI：
10.18637/jss.v039.i05
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发表时间：
2011-03
影响因子：
5.8
通讯作者：
Tibshirani R
中科院分区：
文献类型：
--
作者：
Simon N;Friedman J;Hastie T;Tibshirani R
我们引入了一种由l_1和l_2惩罚（弹性网）的凸组合正则化的Cox比例风险模型的路径算法。我们的算法采用周期性坐标下降拟合，并采用暖起点沿正则化路径寻找解。我们证明了我们的算法在真实和模拟数据集上的有效性，并发现我们的算法与竞争方法之间有相当大的加速。
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.