论文标题
部分可观测时空混沌系统的无模型预测
Robust Estimation of Loss Models for Truncated and Censored Severity Data
论文作者
论文摘要
在本文中，我们考虑对保险中的索赔严重性模型的强有力估计，当数据受截断（由于扣除额）的影响（由于保单限制）和扩展（由于共同保险）的影响时。特别是，基于修剪时刻（T估计器）和温度矩（W-估计器）的方法的稳健估计器被追求并完全开发。制定了此类估计量的一般定义，并研究了其渐近性能。出于说明目的，得出了单参数帕累托分布的尾部参数的t和w-估计器的特定公式。然后，使用著名的挪威火灾索赔数据探索这些估计器的实际性能。我们的结果表明，T-和W-估计器为受免赔额，策略限制和共同保险影响的模型的基于可能性的推断提供了强大且有效的替代方法。
In this paper, we consider robust estimation of claim severity models in insurance, when data are affected by truncation (due to deductibles), censoring (due to policy limits), and scaling (due to coinsurance). In particular, robust estimators based on the methods of trimmed moments (T-estimators) and winsorized moments (W-estimators) are pursued and fully developed. The general definitions of such estimators are formulated and their asymptotic properties are investigated. For illustrative purposes, specific formulas for T- and W-estimators of the tail parameter of a single-parameter Pareto distribution are derived. The practical performance of these estimators is then explored using the well-known Norwegian fire claims data. Our results demonstrate that T- and W-estimators offer a robust and computationally efficient alternative to the likelihood-based inference for models that are affected by deductibles, policy limits, and coinsurance.
