Hazard Regression
使用线性样条及其张量积估计条件对数危险函数，通过最大似然和贝叶斯信息准则自动选择模型，可诊断比例危险假设的偏离，并引入三次样条处理无条件危险函数的尾部灵活性。
Abstract Linear splines and their tensor products are used to estimate the conditional log-hazard function based on possibly censored, positive response data and one or more covariates. An automatic procedure involving the maximum likelihood method, stepwise addition, stepwise deletion, and the Bayes Information Criterion is used to select the final model. The possible models contain proportional hazards models as a subclass, which makes it possible to diagnose departures from proportionality. Cubic splines and two additional log terms are incorporated into a similar model for the unconditional log-hazard function to allow for greater flexibility in the extreme tails. A user interface based on S is described.