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.

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，我们为该模型提出了一个顺序特征选择程序。我们定义了一个新的部分配置文件分数来评估未选择特征对当前模型的影响，从而将重要特征依次添加到模型中，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，我们证明这个过程是选择一致的。进行了广泛的模拟研究和两个真实数据应用程序，以证明我们提出的程序相对于几种代表性方法的优势。