Broken adaptive ridge (BAR) is a computationally scalable surrogate to \(L_0\)-penalized regression, which involves iteratively performing reweighted \(L_2\) penalized regressions and enjoys some appealing properties of both \(L_0\) and \(L_2\) penalized regressions while avoiding some of their limitations. In this paper, we extend the BAR method to the semi-parametric accelerated failure time (AFT) model for right-censored survival data. Specifically, we propose a censored BAR (CBAR) estimator by applying the BAR algorithm to the Leurgan’s synthetic data and show that the resulting CBAR estimator is consistent for variable selection, possesses an oracle property for parameter estimation and enjoys a grouping property for highly correlation covariates. Both low- and high-dimensional covariates are considered. The effectiveness of our method is demonstrated and compared with some popular penalization methods using simulations. Real data illustrations are provided on a diffuse large-B-cell lymphoma data and a glioblastoma multiforme data.

破碎的自适应脊线（BAR）是\（L_0 \）惩罚回归的计算可扩展替代方案，涉及迭代地执行重新加权的（（L_2 \））惩罚回归，并且具有\（L_0 \）和\（L_2 \ ）惩罚性回归，同时避免了某些局限性。在本文中，我们将BAR方法扩展到用于半删失生存数据的半参数加速失败时间（AFT）模型。具体来说，我们通过将BAR算法应用于Leurgan的合成数据，提出了一种经过审查的BAR（CBAR）估计器，并证明了所得的CBAR估计器对于变量选择是一致的，具有用于参数估计的oracle属性，并且对于高相关协变量具有分组属性。低维协变量和高维协变量均被考虑。演示了我们方法的有效性，并与一些流行的使用模拟的惩罚方法进行了比较。在弥散的大B细胞淋巴瘤数据和多形性胶质母细胞瘤数据上提供了真实的数据说明。