The modelling of censored survival data is based on different estimations of the conditional hazard function. When survival time follows a known distribution, parametric models are useful. This strong assumption is replaced by a weaker in the case of semiparametric models. For instance, the frequently used model suggested by Cox is based on the proportionality of hazards. These models use non-parametric methods to estimate some baseline hazard and parametric methods to estimate the influence of a covariate. An alternative approach is to use smoothing that is more flexible. In this paper, two types of kernel smoothing and some bandwidth selection techniques are introduced. Application to real data shows different interpretations for each approach. The extensive simulation study is aimed at comparing different approaches and assessing their benefits. Kernel estimation is demonstrated to be very helpful for verifying assumptions of parametric or semiparametric models and is able to capture changes in the hazard function in both time and covariate directions.

截尾生存数据的建模基于条件风险函数的不同估计。当生存时间遵循已知分布时，参数模型很有用。在半参数模型的情况下，这种强假设被较弱的假设所取代。例如，Cox 建议的常用模型基于危害的比例。这些模型使用非参数方法来估计一些基线风险和参数方法来估计协变量的影响。另一种方法是使用更灵活的平滑。在本文中，介绍了两种类型的核平滑和一些带宽选择技术。对实际数据的应用显示了对每种方法的不同解释。广泛的模拟研究旨在比较不同的方法并评估它们的好处。