论文标题
保险损失数据的强大估计和模型诊断：一种加权可能性方法
Robust estimation and model diagnostic of insurance loss data: a weighted likelihood approach
论文作者
论文摘要
本文为保险损失数据的广义线性模型（GLM）提供了基于得分的加权估计量（SWLE）。 SWLE对异常值表现出有限的敏感性，从理论上讲，其对模型污染的稳健性是合理的。同样，通过专门设计的权重函数可有效减少极端损失对GLM参数估计的贡献，大多数统计数量仍然可以通过分析得出，从而最大程度地减少了参数校准的计算负担。除了强大的估计外，SWLE还可以充当定量诊断工具，以检测异常值和系统模型错误。由覆盖范围修改的促进，通常会随机审查和截断保险损失，SWLE被扩展以容纳审查和截断的数据。我们在三个模拟研究和两个真正的保险数据集上举例说明了SWLE。经验结果表明，如果离群值污染了数据集，则与MLE相比，SWLE产生更可靠的参数估计值。 SWLE诊断工具还成功地检测了具有高功率的任何系统模型错误，并带有一些潜在的模型改进。
This paper presents a score-based weighted likelihood estimator (SWLE) for robust estimations of generalized linear model (GLM) for insurance loss data. The SWLE exhibits a limited sensitivity to the outliers, theoretically justifying its robustness against model contaminations. Also, with the specially designed weight function to effectively diminish the contributions of extreme losses to the GLM parameter estimations, most statistical quantities can still be derived analytically, minimizing the computational burden for parameter calibrations. Apart from robust estimations, the SWLE can also act as a quantitative diagnostic tool to detect outliers and systematic model misspecifications. Motivated by the coverage modifications which make insurance losses often random censored and truncated, the SWLE is extended to accommodate censored and truncated data. We exemplify the SWLE on three simulation studies and two real insurance datasets. Empirical results suggest that the SWLE produces more reliable parameter estimates than the MLE if outliers contaminate the dataset. The SWLE diagnostic tool also successfully detects any systematic model misspecifications with high power, accompanying some potential model improvements.
