An accelerated failure time (AFT) model assuming a log-linear relationship between failure time and a set of covariates can be either parametric or semiparametric, depending on the distributional assumption for the error term. Both classes of AFT models have been popular in the analysis of censored failure time data. The semiparametric AFT model is more flexible and robust to departures from the distributional assumption than its parametric counterpart. However, the semiparametric AFT model is subject to producing biased results for estimating any quantities involving an intercept. Estimating an intercept requires a separate procedure. Moreover, a consistent estimation of the intercept requires stringent conditions. Thus, essential quantities such as mean failure times might not be reliably estimated using semiparametric AFT models, which can be naturally done in the framework of parametric AFT models. Meanwhile, parametric AFT models can be severely impaired by misspecifications. To overcome this, we propose a new type of the AFT model using a nonparametric Gaussian-scale mixture distribution. We also provide feasible algorithms to estimate the parameters and mixing distribution. The finite sample properties of the proposed estimators are investigated via an extensive stimulation study. The proposed estimators are illustrated using a real dataset.

中文翻译：

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通过非参数混合加速故障时间建模

假设故障时间与一组协变量之间存在对数线性关系的加速故障时间 (AFT) 模型可以是参数或半参数，具体取决于误差项的分布假设。两类 AFT 模型在删失失效时间数据的分析中都很流行。半参数 AFT 模型比其参数对应模型更灵活和更稳健地偏离分布假设。然而，半参数 AFT 模型在估计任何涉及截距的量时容易产生有偏差的结果。估计截距需要一个单独的过程。此外，对截距的一致估计需要严格的条件。因此，使用半参数 AFT 模型可能无法可靠地估计基本数量，例如平均故障时间，这可以在参数化 AFT 模型的框架中自然完成。同时，参数化 AFT 模型可能会因规格错误而受到严重损害。为了克服这个问题，我们提出了一种使用非参数高斯尺度混合分布的新型 AFT 模型。我们还提供了可行的算法来估计参数和混合分布。通过广泛的刺激研究研究了所提出的估计量的有限样本特性。所提出的估计量使用真实数据集进行说明。我们还提供了可行的算法来估计参数和混合分布。通过广泛的刺激研究研究了所提出的估计量的有限样本特性。所提出的估计量使用真实数据集进行说明。我们还提供了可行的算法来估计参数和混合分布。通过广泛的刺激研究研究了所提出的估计量的有限样本特性。所提出的估计量使用真实数据集进行说明。