The accelerated failure time model always offers a valuable complement to the traditional Cox proportional hazards model due to its direct and meaningful interpretation. We propose a variable selection method in the context of the accelerated failure time model for survival data, which can simultaneously complete variable selection and parameter estimation. Meanwhile, the proposed method can deal with the potential outliers in survival times as well as heteroscedastic model errors, which are frequently encountered in practice. Specifically, utilizing the general nonconvex penalty, we propose the adaptive penalized weighted least absolute deviation estimator for the accelerated failure time model. Under some regularity conditions, we show that the proposed method yields consistent estimator and possesses the oracle property. In addition, we propose a new algorithm to compute the estimate in the high dimensional settings, and evaluate the practical utility of the proposed method through extensive simulation studies and two real examples.

中文翻译：

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加速失效时间模型的自适应惩罚加权最小绝对偏差估计

由于其直接且有意义的解释，加速故障时间模型始终为传统的 Cox 比例风险模型提供有价值的补充。我们提出了一种在加速失效时间模型背景下生存数据的变量选择方法，可以同时完成变量选择和参数估计。同时，所提出的方法可以处理生存时间中的潜在异常值以及在实践中经常遇到的异方差模型错误。具体来说，利用一般的非凸惩罚，我们为加速失效时间模型提出了自适应惩罚加权最小绝对偏差估计器。在一定的规律性条件下，我们表明所提出的方法产生一致的估计量并具有预言性。此外，