The instability in the selection of models is a major concern with data sets containing a large number of covariates. We focus on stability selection which is used as a technique to improve variable selection performance for a range of selection methods, based on aggregating the results of applying a selection procedure to sub-samples of the data where the observations are subject to right censoring. The accelerated failure time (AFT) models have proved useful in many contexts including the heavy censoring (as for example in cancer survival) and the high dimensionality (as for example in micro-array data). We implement the stability selection approach using three variable selection techniques-Lasso, ridge regression, and elastic net applied to censored data using AFT models. We compare the performances of these regularized techniques with and without stability selection approaches with simulation studies and two real data examples-a breast cancer data and a diffuse large B-cell lymphoma data. The results suggest that stability selection gives always stable scenario about the selection of variables and that as the dimension of data increases the performance of methods with stability selection also improves compared to methods without stability selection irrespective of the collinearity between the covariates.

中文翻译：

---

用AFT模型实现的套索，山脊和弹性网的稳定性选择。

对于包含大量协变量的数据集，模型选择的不稳定性是一个主要问题。我们将重点放在稳定性选择上，该技术被用作一种技术，用于提高一系列选择方法的变量选择性能，其基础是对对观察结果进行正确审查的数据子样本应用选择程序的结果进行汇总。事实证明，加速失败时间（AFT）模型在许多情况下都是有用的，包括严格的检查（例如在癌症生存中）和高维度（例如在微阵列数据中）。我们使用三种变量选择技术（套索，岭回归和弹性网）使用AFT模型实施稳定性选择方法，该技术应用于套索数据。我们通过仿真研究和两个真实数据示例（乳腺癌数据和弥漫性大B细胞淋巴瘤数据），比较了使用和不使用稳定性选择方法的这些正规化技术的性能。结果表明，稳定性选择始终为变量选择提供稳定的方案，并且随着数据维数的增加，具有稳定性选择的方法的性能也比不具有稳定性选择的方法有所提高，而与协变量之间的共线性无关。