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：在再保险中，泊松分布和负二项分布被用于对频数进行建模。然而，关于优先层级之上已报告已发生赔款的不完整数据给参数估计带来了挑战。本文重点研究基于Schnieper理赔编号框架的频数估计。我们证明Schnieper模型与每年发展中优先层级之上总索赔次数的泊松分布一致，为参数估计提供了稳健基础。此外，我们阐释了如何构建基于负二项分布的替代假设，该假设能产生相似结果。本研究包含用于管理参数估计不确定性的自举程序，并通过案例研究比较不同假设、评估自举方法的影响。