A note on path-based variable selection in the penalized proportional hazards model
A note on path-based variable selection in the penalized proportional hazards model
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DOI：
10.1093/biomet/asm083
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发表时间：
2008-03-01
期刊：
影响因子：
2.7
通讯作者：
Zou, Hui
中科院分区：
文献类型：
--
作者：
Zou, Hui
我们提出了一种用于 Cox 模型中变量选择的高效自适应收缩方法。该方法构造连接最大偏似然估计器和原点的分段线性正则化路径。然后沿着路径选择一个模型。我们表明，构建的路径是自适应的，因为通过正确选择正则化参数，拟合模型的效果与提前给出真实的基础子模型的效果一样好。最小角度回归类型的修改算法有效地计算新估计器的整个正则化路径。此外，我们表明，通过正确选择收缩参数，该方法在变量选择上是一致的并且估计是有效的。仿真表明，新方法往往优于套索和中等样本的平滑裁剪绝对偏差估计器。我们将该方法应用于有关疗养院的数据。
We propose an efficient and adaptive shrinkage method for variable selection in the Cox model. The method constructs a piecewise-linear regularization path connecting the maximum partial likelihood estimator and the origin. Then a model is selected along the path. We show that the constructed path is adaptive in the sense that, with a proper choice of regularization parameter, the fitted model works as well as if the true underlying submodel were given in advance. A modified algorithm of the least-angle-regression type efficiently computes the entire regularization path of the new estimator. Furthermore, we show that, with a proper choice of shrinkage parameter, the method is consistent in variable selection and efficient in estimation. Simulation shows that the new method tends to outperform the lasso and the smoothly-clipped-absolute-deviation estimators with moderate samples. We apply the methodology to data concerning nursing homes.