Projection‐based and cross‐validated estimation in high‐dimensional Cox model
Projection‐based and cross‐validated estimation in high‐dimensional Cox model
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DOI：
10.1111/sjos.12515
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发表时间：
2021-03
影响因子：
1
通讯作者：
Haixiang Zhang;Jian Huang;Liuquan Sun
中科院分区：
文献类型：
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作者：
Haixiang Zhang;Jian Huang;Liuquan Sun
提出了一种基于投影的交叉验证方法，用于在考克斯回归模型中存在高维干扰参数的情况下估计低维参数.我们证明了所提出的估计量是渐近正态的，这使得我们能够对具有高维讨厌参数的感兴趣参数进行假设检验。提出了三种决策规则，避免了随机样本分裂的影响。模拟研究表明，当预测因子的系数是高维的并且不是很稀疏时，我们的方法比Fang等人的方法更强大（2017，JRSSB）。作为一个说明性的例子，我们将我们的程序应用于乳腺癌研究。
We propose a projection‐based cross‐validation method for estimating a low‐dimensional parameter in the presence of a high‐dimensional nuisance parameter in the Cox regression model. We show that the proposed estimator is asymptotically normal, which enables us to conduct hypothesis test for the parameter of interest with high‐dimensional nuisance parameters. Three decision rules are presented to avoid the influence of random splitting of samples. Simulation studies indicate that our method is more powerful than that of Fang et al. (2017, JRSSB) when the coefficients of predictors are high‐dimensional and not very sparse. As an illustrative example, we apply our procedure to a breast cancer study.