A sequential feature selection procedure for high-dimensional Cox proportional hazards model

Yu Ke; Luo Shan

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A sequential feature selection procedure for high-dimensional Cox proportional hazards model

机译：高维Cox比例风险模型的顺序特征选择过程

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Feature selection for the high-dimensional Cox proportional hazards model (Cox model) is very important in many microarray genetic studies. In this paper, we propose a sequential feature selection procedure for this model. We define a novel partial profile score to assess the impact of unselected features conditional on the current model, significant features are thereby added into the model sequentially, and the Extended Bayesian Information Criteria (EBIC) is adopted as a stopping rule. Under mild conditions, we show that this procedure is selection consistent. Extensive simulation studies and two real data applications are conducted to demonstrate the advantage of our proposed procedure over several representative approaches.

机译：高维Cox比例风险模型（Cox模型）的特征选择在许多微阵列遗传学研究中非常重要。在本文中，我们提出了该模型的顺序特征选择过程。我们定义了一种新的部分剖面评分来评估以当前模型为条件的未选择特征的影响，从而按顺序将重要特征添加到模型中，并采用扩展贝叶斯信息准则（EBIC）作为停止规则。在温和的条件下，我们证明该程序的选择是一致的。进行了广泛的模拟研究和两个真实数据应用，以证明我们提出的程序相对于几种代表性方法的优势。

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来源
《Annals of the Institute of Statistical Mathematics》 |2022年第6期|1109-1142|共34页
作者
Yu Ke; Luo Shan;
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作者单位

Shanghai Jiao Tong Univ;

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原文格式 PDF
正文语种英语
中图分类数理统计;
关键词
Sequential feature selection; Selection consistency; Cox proportional hazards model; High-dimensional; Extended Bayesian information criteria; VARIABLE SELECTION; ADAPTIVE LASSO; REGRESSION; REGULARIZATION;

机译：顺序特征选择;选择一致性;Cox比例风险模型;高维;扩展贝叶斯信息准则;变量选择;自适应 LASSO;回归;正规化;

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A sequential feature selection procedure for high-dimensional Cox proportional hazards model

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