论文标题
破裂的自适应山脊回归用于右验证的生存数据
Broken Adaptive Ridge Regression for Right-Censored Survival Data
论文作者
论文摘要
破碎的自适应山脊（BAR）是计算上可扩展的替代替代$ l_0 $ penalizatizatization回归，涉及迭代执行重新处理的$ L_2 $惩罚性回归，并享受$ L_0 $和$ L_2 $的罚款的一些吸引人的属性，同时避免了他们的某些局限性。在本文中，我们将BAR方法扩展到右审查生存数据的半参数加速故障时间（AFT）模型。具体而言，我们通过将BAR算法应用于Leurgan的合成数据，并表明所得的CBAR估计器对于可变选择是一致的，它具有参数估计的Oracle属性{并享受高度相关covariates的分组属性}。都考虑了低维协变量。证明了我们方法的有效性，并与使用模拟的一些流行惩罚方法进行了比较。在弥漫性大细胞淋巴瘤数据和胶质母细胞瘤多形数据上提供了真实的数据图。
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.
