Joint model-free feature screening for ultra-high dimensional semi-competing risks data
Joint model-free feature screening for ultra-high dimensional semi-competing risks data
复制标题
超高维半竞争风险数据的联合无模型特征筛选
DOI：
10.1016/j.csda.2020.106942
复制
发表时间：
2020-07
影响因子：
1.8
通讯作者：
Liu Chunling
中科院分区：
文献类型：
--
作者：
鲁水云;陈晓林;Xu Sheng;Liu Chunling
在生物医学研究中，通常会出现由两个可能相关的事件（终末事件和非终末事件）组成的高维半竞争风险数据。然而，相应的统计分析很少被研究。提出了一种用于终端事件和非终端事件的联合无模型特征筛选方法，该方法允许相关协变量处于超高维特征空间中。联合筛选效用是由每个预测因子的生存函数与终末事件和非终末事件的联合生存函数之间的距离相关性构造的。在相当温和的技术假设下，证明了所提出的联合特征筛选过程具有一定的筛选性和一致性的排名属性。进一步提出了一种自适应阈值规则，以同时识别重要的协变量，并确定这些协变量的数量。进行了大量的数值研究，以检查所提出的方法的有限样本性能。最后，通过一个真实的例子说明了所建议的联合特征筛选过程。
High-dimensional semi-competing risks data consisting of two probably correlated events, namely terminal event and non-terminal event, arise commonly in many biomedical studies. However, the corresponding statistical analysis is rarely investigated. A joint model-free feature screening procedure for both terminal and non-terminal events is proposed, which could allow the associated covariates to be in an ultra-high dimensional feature space. The joint screening utility is constructed from distance correlation between each predictor’s survival function and joint survival function of terminal and non-terminal events. Under rather mild technical assumptions, it is demonstrated that the proposed joint feature screening procedure enjoys sure screening and consistency in ranking properties. An adaptive threshold rule is further suggested to simultaneously identify important covariates and determine number of these covariates. Extensive numerical studies are conducted to examine the finite-sample performance of the proposed methods. Lastly, the suggested joint feature screening procedure is illustrated through a real example.
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影响因子：
2
作者：
Kong, Jing;Wang, Sijian;Wahba, Grace
通讯作者：
Wahba, Grace
DOI：
10.1080/01621459.2013.850086
发表时间：
2014-01-01
影响因子：
3.7
作者：
Liu J;Li R;Wu R
通讯作者：
Wu R
影响因子：
0.8
作者：
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通讯作者：
Xiaolin Chen;Yahui Zhang;Yi Liu;Xiaojing Chen
影响因子：
1.9
作者：
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通讯作者：
Abdous, Belkacem
影响因子：
2.7
作者：
Fine, JP;Jiang, H;Chappell, R
通讯作者：
Chappell, R