IDENTIFIABILITY OF THE SIGN OF COVARIATE EFFECTS IN THE COMPETING RISKS MODEL
提出一个识别竞争风险模型的新框架，在弱约束下通过希克斯型分解识别协变量对边际分布的影响方向，模拟和实例表明该方法在长持续时间上优于现有方法。
We present a new framework for the identification of competing risks models, which also include Roy models. We show that by establishing a Hicksian-type decomposition, the direction of covariate effects on the marginal distributions of the competing risks model can be identified under weak restrictions. Our approach leaves the marginal distributions and their joint distribution completely unspecified, except that the associated copula is invariant in the covariates. Results from simulations and two data examples suggest that our method often outperforms existing comparable approaches in terms of the range of durations for which the direction of the covariate effect is identified, particularly for long duration.