Ahn Kwang Woo, Banerjee Anjishnu, Sahr Natasha, Kim Soyoung
Division of Biostatistics, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA.
Lifetime Data Anal. 2018 Jul;24(3):407-424. doi: 10.1007/s10985-017-9400-9. Epub 2017 Aug 4.
Variable selection in the presence of grouped variables is troublesome for competing risks data: while some recent methods deal with group selection only, simultaneous selection of both groups and within-group variables remains largely unexplored. In this context, we propose an adaptive group bridge method, enabling simultaneous selection both within and between groups, for competing risks data. The adaptive group bridge is applicable to independent and clustered data. It also allows the number of variables to diverge as the sample size increases. We show that our new method possesses excellent asymptotic properties, including variable selection consistency at group and within-group levels. We also show superior performance in simulated and real data sets over several competing approaches, including group bridge, adaptive group lasso, and AIC / BIC-based methods.
在存在分组变量的情况下，竞争风险数据的变量选择很麻烦：虽然最近的一些方法仅处理组选择，但组和组内变量的同时选择在很大程度上仍未得到探索。在这种情况下，我们提出了一种自适应组桥方法，用于竞争风险数据，可以同时在组内和组间进行选择。自适应组桥适用于独立数据和聚类数据。它还允许变量数量随着样本量的增加而发散。我们表明，我们的新方法具有出色的渐近性质，包括组和组内水平的变量选择一致性。我们还表明，在模拟数据集和真实数据集中，我们的方法比几种竞争方法表现更优，包括组桥、自适应组套索和基于AIC / BIC的方法。
