Inferential challenges that arise when data are censored have been extensively studied under the classical frameworks. In this paper, we provide an alternative generalized inferential model approach whose output is a data-dependent plausibility function. This construction is driven by an association between the distribution of the relative likelihood function at the interest parameter and an unobserved auxiliary variable. The plausibility function emerges from the distribution of a suitably calibrated random set designed to predict that unobserved auxiliary variable. The evaluation of this plausibility function requires a novel use of the classical Kaplan–Meier estimator to estimate the censoring rather than the event distribution. We prove that the proposed method provides valid inference, at least approximately, and our real- and simulated-data examples demonstrate its superior performance compared to existing methods.

在经典框架下广泛研究了数据审查时出现的推理挑战。在本文中，我们提供了一种替代的广义推理模型方法，其输出是依赖于数据的似真函数。这种构造是由兴趣参数处的相对似然函数分布与未观察到的辅助变量之间的关联驱动的。似然函数来自一个适当校准的随机集的分布，该随机集旨在预测未观察到的辅助变量。对该似然函数的评估需要使用经典的 Kaplan-Meier 估计器来估计删失而不是事件分布。我们证明所提出的方法提供了有效的推理，至少近似地，