Variable selection and structure identification for varying coefficient Cox models
Variable selection and structure identification for varying coefficient Cox models
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DOI：
10.1016/j.jmva.2017.07.007
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发表时间：
2016-07
期刊：
影响因子：
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通讯作者：
Toshio Honda;Ryota Yabe
中科院分区：
文献类型：
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作者：
Toshio Honda;Ryota Yabe
我们考虑具有高维协变量的变系数 Cox 模型。我们将组套索应用于这些模型并提出变量选择程序。我们的程序可以处理高维变系数模型的同时变量选择和结构识别，从而从中找到真正的半变系数模型。我们还推导了预言不等式并仔细研究了限制性特征值条件。我们关注具有时变系数的 Cox 模型。关于变量选择的理论结果可以很容易地扩展到其他一些重要的模型，我们只是简单地提到这些模型，因为它们可以以相同的方式处理。这里考虑的模型是结构化非参数回归模型中最流行的模型。还报告了数值研究的结果。
We consider varying coefficient Cox models with high-dimensional covariates. We apply the group Lasso to these models and propose a variable selection procedure. Our procedure can cope with simultaneous variable selection and structure identification for high-dimensional varying coefficient models to find true semi-varying coefficient models from them. We also derive an oracle inequality and closely examine restrictive eigenvalue conditions. We focus on Cox models with time-varying coefficients. The theoretical results on variable selection can be extended easily to some other important models which we only mention briefly since they can be treated in the same way. The models considered here are the most popular among structured nonparametric regression models. The results of numerical studies are also reported.