Censored and extreme losses: Functional convergence and applications to tail goodness-of-fit
建立了极端Nelson-Aalen和极端Kaplan-Meier估计量的函数收敛性，用于捕捉删失损失的重尾行为，并基于此提出了两种选择顺序统计量个数的规则，通过模拟和法国车险理赔数据验证了有效性。
This paper establishes the functional convergence of the Extreme Nelson–Aalen and Extreme Kaplan–Meier estimators, which are designed to capture the heavy-tailed behaviour of censored losses. The resulting limit representations can be used to obtain the distributions of functionals with respect to the so-called tail process. For instance, we may recover the convergence of a censored Hill estimator, and we further investigate two goodness-of-fit statistics for the tail of the loss distribution. Using the latter limit theorems, we propose two rules for selecting a suitable number of order statistics, both based on test statistics derived from the functional convergence results. The effectiveness of these selection rules is investigated through simulations and an application to a real dataset comprised of French motor insurance claim sizes.