The Non-Parametric Identification of Generalized Accelerated Failure-Time Models
研究广义加速失效时间模型，证明其可非参数识别，并重新审视混合比例风险模型的识别条件，指出混合分布有限均值是必要前提。
We consider a class of models that generalizes the popular Mixed Proportional Hazard (MPH) model for duration data: the Generalized Accelerated Failure-Time (GAFT) model. We show that the GAFT model is non-parametrically identified (up to a normalization). We then reconsider the non-parametric identification of the MPH model. We show that the class of MPH models is not closed under normalization. This implies that a finite mean of the mixing distribution is a necessary condition for (non-parametric) identification of the MPH model. It is impossible to test this hypothesis without imposing arbitrary restrictions on the base-line hazard and/or the regression function.