广义XGBoost方法
Generalized XGBoost Method
摘要：
XGBoost方法有很多优点，特别适合大数据的统计分析，但其损失函数仅限于凸函数。在许多具体应用中，非凸损失函数将是优选的。本文提出了一种广义的XGBoost方法，该方法对损失函数的约束条件要求较弱，并且涉及到更一般的损失函数，包括凸的损失函数和一些非凸的损失函数。进一步，将该方法推广到多元损失函数，得到了一种更广义的XGBoost方法.该方法是一种多目标参数正则化树Boosting方法，可以对大多数常用的参数概率分布中的多个参数进行建模，并由预测变量进行拟合。同时，给出了非寿险定价中的相关算法和实例。
英文摘要：
The XGBoost method has many advantages and is especially suitable for statistical analysis of big data, but its loss function is limited to convex functions. In many specific applications, a nonconvex loss function would be preferable. In this paper, I propose a generalized XGBoost method, which requires weaker loss function constraint and involves more general loss functions, including convex loss functions and some non-convex loss functions. Furthermore, this generalized XGBoost method is extended to multivariate loss function to form a more generalized XGBoost method. This method is a multiobjective parameter regularized tree boosting method, which can model multiple parameters in most of the frequently-used parametric probability distributions to be fitted by predictor variables. Meanwhile, the related algorithms and some examples in non-life insurance pricing are given.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
