论文标题
知情审查：数据和专家信息的参数组合
Informed censoring: the parametric combination of data and expert information
论文作者
论文摘要
当可以将随机度量分配给数据标记的实现时，统计审查设置扩展到了情况，从而导致了将专家信息纳入常规参数估计过程的新方法。为结果估计器提供了渐近理论，并更详细地研究了一些实用相关性的特殊案例。尽管所提出的框架数学上可以随机地概括和重新审查，并借用了M估计理论的技术，但它提供了一种新颖而透明的方法，该方法在存在专家信息的情况下具有重要的实际适用性。该方法的潜力通过对重尾MTPL数据集的尾部参数估计的具体精算应用，具有有限的可用专家信息。
The statistical censoring setup is extended to the situation when random measures can be assigned to the realization of datapoints, leading to a new way of incorporating expert information into the usual parametric estimation procedures. The asymptotic theory is provided for the resulting estimators, and some special cases of practical relevance are studied in more detail. Although the proposed framework mathematically generalizes censoring and coarsening at random, and borrows techniques from M-estimation theory, it provides a novel and transparent methodology which enjoys significant practical applicability in situations where expert information is present. The potential of the approach is illustrated by a concrete actuarial application of tail parameter estimation for a heavy-tailed MTPL dataset with limited available expert information.
