标题： 一种基于算法推理的学习copula的方法 显示英文标题

标题： An Algorithmic Inference Approach to Learn Copulas

摘要： 我们引入了一种在算法推理框架下估计双变量Claytoncopula参数的新方法。该方法是对标准自助法推断随机参数程序的一种变体，我们专门设计此变体以规避这一特定实例的两个陷阱：Kendall统计量的非独立性（通常是此推断任务的基础）以及相对于\alpha 缺乏充分统计量。该变体基于一种数值程序，用于在迭代过程的固定点找到\alpha 的估计值。尽管与计算这些估计值的程序通常的复杂性相结合，数值结果显示了这些估计值的卓越准确性。 显示英文摘要

摘要： We introduce a new method for estimating the parameter of the bivariate Clayton copulas within the framework of Algorithmic Inference. The method consists of a variant of the standard boot-strapping procedure for inferring random parameters, which we expressly devise to bypass the two pitfalls of this specific instance: the non independence of the Kendall statistics, customarily at the basis of this inference task, and the absence of a sufficient statistic w.r.t. \alpha. The variant is rooted on a numerical procedure in order to find the \alpha estimate at a fixed point of an iterative routine. Although paired with the customary complexity of the program which computes them, numerical results show an outperforming accuracy of the estimates.