A methodology for assessing the inputs-outputs association for time-depending predictive models under failure mode for instance is investigated. Firstly, new dependency models for sampling random values of uncertain inputs that comply with the safety objectives are provided. Secondly, the asymmetric role of outputs and inputs leads to develop new kernel-based statistical tests of independence between the inputs and outputs using the dependency models. The associated test statistics are normalized so as to introduce new kernel-based sensitivity indices (Kb-SIs). Such first-order and total Kb-SIs allow for i) assessing the inputs effects on the whole dynamic outputs subjected to safety objectives, ii) dealing with sensitivity functionals (SFs) having heavy-tailed distributions or non-stationary time-depending SFs thanks to kernel methods. Our approach is also well-suited for dynamic models with prescribed copulas of inputs.

中文翻译：

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使用内核测量安全目标下随时间变化的危险模型的输入-输出关联

例如，研究了一种评估失效模式下瞬态预测模型的输入输出关联的方法。首先，提供了新的依赖模型，用于对符合安全目标的不确定输入的随机值进行采样。其次，输出和输入的不对称作用导致使用依赖模型开发新的基于内核的输入和输出之间独立性的统计测试。相关的测试统计数据被标准化，以引入新的基于内核的敏感度指数（Kb-SI）。这种一阶和总 Kb-SI 允许 i) 评估输入对符合安全目标的整个动态输出的影响，ii) 处理具有重尾分布或非平稳时间相关 SF 的敏感泛函 (SF)到内核​​方法。我们的方法也非常适合具有指定输入联结的动态模型。