论文标题
稳定估计损失发展因素的非凸正规化方法
A non-convex regularization approach for stable estimation of loss development factors
论文作者
论文摘要
在本文中，我们采用非凸正规化方法，以便获得保留保险索赔中损失发展因素的稳定估计。在非凸正则化方法中，我们着重于使用对数量调整的绝对偏差（LAAD）惩罚，并就LAAD惩罚回归模型的优化进行了讨论，我们证明这是在轻度条件下与坐标下降算法相聚的。这具有对回归系数的一致估计器的优势，同时允许选择变量，这与损失发展因子的稳定估计有关。我们使用从财产和伤亡保险公司的多线保险数据集校准了我们的拟议模型，我们观察到了事故年和发展期间报告的总损失。与其他回归模型相比，我们的LAAD惩罚回归模型提供了非常有希望的结果。
In this article, we apply non-convex regularization methods in order to obtain stable estimation of loss development factors in insurance claims reserving. Among the non-convex regularization methods, we focus on the use of the log-adjusted absolute deviation (LAAD) penalty and provide discussion on optimization of LAAD penalized regression model, which we prove to converge with a coordinate descent algorithm under mild conditions. This has the advantage of obtaining a consistent estimator for the regression coefficients while allowing for the variable selection, which is linked to the stable estimation of loss development factors. We calibrate our proposed model using a multi-line insurance dataset from a property and casualty insurer where we observed reported aggregate loss along accident years and development periods. When compared to other regression models, our LAAD penalized regression model provides very promising results.
