The global sensitivity analysis method, used to quantify the influence of uncertain input variables on the response variability of a numerical model, is applicable to deterministic computer code (for which the same set of input variables gives always the same output value). This paper proposes a global sensitivity analysis methodology for stochastic computer code (having a variability induced by some uncontrollable variables). The framework of the joint modeling of the mean and dispersion of heteroscedastic data is used. To deal with the complexity of computer experiment outputs, non parametric joint models (based on Generalized Additive Models and Gaussian processes) are discussed. The relevance of these new models is analyzed in terms of the obtained variance-based sensitivity indices with two case studies. Results show that the joint modeling approach leads accurate sensitivity index estimations even when clear heteroscedasticity is present.
Estimation and model errors in Gaussian-process-based Sensitivity Analysis of functional outputs
Institut de Mathématiques de Toulouse, Université de Toulouse（图卢兹数学研究所，图卢兹大学）；São Paulo State University “Júlio de Mesquita Filho” (UNESP)（圣保罗州立大学（朱利奥·德·梅斯基塔·菲略））
The ICSCREAM methodology: Identification of penalizing configurations in computer experiments using screening and metamodel -- Applications in thermal-hydraulics
Learning Global Sensitivity Indices from Observational Data: A Metamodel-Based Approach
University of Naples Federico II（那不勒斯费德里科二世大学）；Polytechnic and Basic Sciences School（理工与基础科学学院）；Department of Electrical Engineering and Information Technology（电气与信息工程系）
A weighting method for simultaneous adjustment for confounding and joint exposure-outcome misclassifications
暴露和结果变量的联合错误分类可能导致因果关系确定-结果效应的流行病学研究中出现相当大的偏倚。在本文中，我们提出了一个新的极大似然估计的边际因果比值比，同时调整混杂和几种形式的联合误分类的暴露和结果变量。所提出的方法依赖于验证数据的建设的权重，占两个来源的偏见。加权估计量是Gravel和Platt（Statistics in Medicine，2018）提出的暴露误分类加权估计量的扩展，适用于再梗死数据。进行了模拟研究，研究其有限样本的性质，并将其与不考虑混杂或误分类的方法进行比较。新的估计显示出有利的大样本性能的模拟。需要进一步研究所提出的方法的敏感性，以及违反其假设的替代方案。在现有的R包中的一个新的R函数的估计的实施是方便的。
