论文标题
一般度量空间中的灵敏度分析
Sensitivity analysis in general metric spaces
论文作者
论文摘要
在本文中，我们介绍了适用于一般度量空间中价值的输出的新索引。这类新的索引涵盖了古典索引。特别是，所谓的索博指数和cram {é} r-von-misises索引。此外，我们基于U统计数据提供了这些指数的渐近高斯估计量。令人惊讶的是，我们直接证明了Asymp-Tosic正态性。最后，我们在玩具模型和两个真实数据示例上说明了这一新程序。
In this paper, we introduce new indices adapted to outputs valued in general metric spaces. This new class of indices encompasses the classical ones; in particular, the so-called Sobol indices and the Cram{é}r-von-Mises indices. Furthermore, we provide asymptotically Gaussian estimators of these indices based on U-statistics. Surprisingly, we prove the asymp-totic normality straightforwardly. Finally, we illustrate this new procedure on a toy model and on two real-data examples.
