In reinsurance, Poisson and Negative binomial distributions are employed for modeling frequency. However, the incomplete data regarding reported incurred claims above a priority level presents challenges in estimation. This paper focuses on frequency estimation using Schnieper's framework for claim numbering. We demonstrate that Schnieper's model is consistent with a Poisson distribution for the total number of claims above a priority at each year of development, providing a robust basis for parameter estimation. Additionally, we explain how to build an alternative assumption based on a Negative binomial distribution, which yields similar results. The study includes a bootstrap procedure to manage uncertainty in parameter estimation and a case study comparing assumptions and evaluating the impact of the bootstrap approach.

翻译：在再保险领域，泊松分布与负二项分布常用于频率建模。然而，高于优先级水平的已报告已发生索赔数据不完整，为参数估计带来挑战。本文基于Schnieper索赔计数框架进行频率估计研究。我们证明在每年度进展中，Schnieper模型与优先级以上索赔总数的泊松分布具有一致性，这为参数估计提供了稳健基础。此外，我们阐述了如何基于负二项分布构建替代假设，该假设可获得相似结果。本研究包含用于管理参数估计不确定性的自助法程序，并通过案例研究比较不同假设，评估自助法的影响效果。