This paper studies identification and inference in transformation models with endogenous censoring. Many kinds of duration models, such as the accelerated failure time model, proportional hazard model, and mixed proportional hazard model, can be viewed as transformation models. We allow the censoring of a duration outcome to be arbitrarily correlated with observed covariates and unobserved heterogeneity. We impose no parametric restrictions on either the transformation function or the distribution function of the unobserved heterogeneity. In this setting, we develop bounds on the regression parameters and the transformation function, which are characterized by conditional moment inequalities involving U-statistics. We provide inference methods for them by constructing an inference approach for conditional moment inequality models in which the sample analogs of moments are U-statistics. We apply the proposed inference methods to evaluate the effect of heart transplants on patients' survival time using data from the Stanford Heart Transplant Study.
翻译:本文用内生审查来研究转换模型的识别和推断。许多周期模型,如加速失灵时间模型、成比例危险模型和混合成比例危险模型,可以视为转换模型。我们允许任意审查持续结果与观察到的共变和未观察到的异质性相联系。我们没有对未观察到的异质性的变化功能或分布功能施加任何参数限制。在这个环境中,我们开发了回归参数和转换功能的界限,其特点是涉及U-统计学的有条件的时段不平等。我们为它们提供了推断方法,为有条件的时段不平等模型制定了一种推论方法,其中的时段样本模拟为U-统计学。我们采用拟议的推论方法,利用斯坦福心脏移植研究的数据评估心脏移植对病人存活时间的影响。