NONPARAMETRIC IDENTIFICATION OF THE MIXED HAZARDS MODEL WITH TIME-VARYING COVARIATES
证明，在存在标准时变协变量的情况下，无需比例风险假设即可识别混合风险模型，仅需协变量随时间的变化与观测间的变异。
Most nonparametric identification results for the mixed proportional hazards model for single spell duration data depend crucially on the proportional hazards assumption. Here, it is shown that variation in covariates over time, combined with variation across observations, is sufficient to ensure identification without the proportional hazards assumption. The required variation over time is minimal, and the mixed hazards model is identified without the proportional hazards assumption in the presence of standard time-varying covariates.Thanks to Kåre Bævre, Zhiyang Jia, Tore Schweder, Rolf Aaberge, and John K. Dagsvik for useful comments.