The accelerated failure time (AFT) model is widely used to analyze relationships between variables in the presence of censored observations. However, this model relies on some assumptions such as the error distribution, which can lead to biased or inefficient estimates if these assumptions are violated. In order to overcome this challenge, we propose a novel approach that incorporates a semiparametric skew-normal scale mixture distribution for the error term in the AFT model. By allowing for more flexibility and robustness, this approach reduces the risk of misspecification and improves the accuracy of parameter estimation. We investigate the identifiability and consistency of the proposed model and develop a practical estimation algorithm. To evaluate the performance of our approach, we conduct extensive simulation studies and real data analyses. The results demonstrate the effectiveness of our method in providing robust and accurate estimates in various scenarios.

翻译：加速失效时间（AFT）模型广泛应用于存在删失观测场景下变量间关系的分析。然而，该模型依赖于误差分布等假设，若这些假设被违反，可能导致估计结果产生偏倚或效率低下。为克服这一挑战，本研究提出一种新型方法，在AFT模型中引入半参数偏态正态尺度混合分布作为误差项。该方法通过提升灵活性与稳健性，降低了模型误设风险，并提高了参数估计的准确性。我们研究了所提模型的可识别性与相合性，开发了实用的估计算法，并通过大规模模拟实验与真实数据分析评估了模型性能。结果表明，该方法能够在多种场景下提供稳健且准确的估计。