发表机构
Management Development Institute of Singapore in Tashkent（新加坡管理学院塔什干分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对竞争风险模型，利用类别排除限制和原因指标，提出可反驳的识别检验方法，并构建稳健置信集，模拟验证其有效性。
AI 中文摘要
竞争风险数据在缺乏额外限制的情况下无法识别潜在的边际持续时间分布或其依赖关系。本文探讨的问题是：为恢复可识别性而引入的限制本身是否限制了可观测的规律。对于具有类别排除限制的双风险阿基米德模型，我们推导出一个充要的可观测特征刻画。一个离散单交叉论证从单元特定的总体生存概率中识别出标量copula参数，而原因指标则恢复剩余分配并产生额外的规格限制。我们构建了一个对识别稳健的自归一化二次统计量，对其求逆以获得置信集，并将空的反演集用作保守的规格检验。模拟表明，原因指标可能是决定性的：在所考虑的最弱对比下，它将在无信息性的基于生存的置信集转换为信息丰富的联合集且不损失覆盖率，而过度的类别对比可能消除单个单元中的原因并使原因特定恢复不可行。这些结果将潜在的排除限制转化为可反驳的限制，而无需估计协变量导数。
英文摘要
Competing-risks data do not identify latent marginal duration distributions or their dependence without additional restrictions. This paper asks whether restrictions introduced to restore identification themselves restrict the observable law. For a two-risk Archimedean model with categorical exclusion restrictions, we derive a necessary-and-sufficient observable characterization. A discrete single-crossing argument identifies the scalar copula parameter from cell-specific overall survival probabilities, while cause indicators recover the remaining allocation and generate additional specification restrictions. We construct an identification-robust, self-normalized quadratic statistic, invert it to obtain confidence sets, and use empty inverted sets as a conservative specification test. Simulations show that the cause indicator can be decisive: under the weakest contrast considered it converts a frequently uninformative survival-based confidence set into an informative joint set without loss of coverage, whereas excessive categorical contrast can eliminate causes from individual cells and make cause-specific recovery inadmissible. The results turn latent exclusion restrictions into refutable restrictions without estimating covariate derivatives.
