The non-identifiability of the competing risks model requires researchers to work with restrictions on the model to obtain informative results. We present a new identifiability solution based on an exclusion restriction. Many areas of applied research use methods that rely on exclusion restrcitions. It appears natural to also use them for the identifiability of competing risks models. By imposing the exclusion restriction couple with an Archimedean copula, we are able to avoid any parametric restriction on the marginal distributions. We introduce a semiparametric estimation approach for the nonparametric marginals and the parametric copula. Our simulation results demonstrate the usefulness of the suggested model, as the degree of risk dependence can be estimated without parametric restrictions on the marginal distributions.

翻译：竞争风险模型的不可识别性要求研究者对模型施加限制以获得有信息量的结果。本文提出了一种基于排除限制的新的可识别性解决方案。许多应用研究领域使用依赖排除限制的方法，将其用于竞争风险模型的可识别性也显得十分自然。通过将排除限制与阿基米德copula相结合，我们能够避免对边际分布施加任何参数限制。我们引入了一种半参数估计方法，用于处理非参数边际分布和参数copula。我们的模拟结果表明，所提出的模型具有实用性，因为可以在不对边际分布施加参数限制的情况下估计风险依赖程度。