自适应加速失败时间建模与半参数偏斜误差分布
Adaptive Accelerated Failure Time modeling with a Semiparametric Skewed Error Distribution
AI总结：
本文提出一种基于半参数偏斜误差分布的自适应加速失败时间模型，以提高参数估计的鲁棒性和准确性。
摘要：
加速失败时间模型广泛用于在存在删失观测的情况下分析变量间的关系。然而，该模型依赖于一些假设，如误差分布，如果这些假设被违反，可能导致估计偏误或低效率。为克服这一挑战，我们提出了一种新的方法，将半参数偏斜正态尺度混合分布纳入AFT模型的误差项中。通过允许更多的灵活性和鲁棒性，这种方法减少了规格错误的风险，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和一致性，并开发了实用的估计算法。为了评估我们方法的性能，我们进行了广泛的模拟研究和实际数据分析。结果表明，我们的方法在各种场景中提供了稳健且准确的估计。
英文摘要：
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.
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