摘要翻译：
复杂的计算模型越来越多地被企业和政府用于决策，如如何投资以及在哪里投资以过渡到低碳世界。大规模模型产生的结果具有很大的复杂性，因此需要使用先进的灵敏度分析技术。据我们所知，没有任何方法能够对比标量输出更复杂的输出进行灵敏度分析，也没有任何方法能够使用健全的统计框架来处理模型的不确定性。这项工作的目的是解决这两个缺点，结合灵敏度和功能数据分析。我们使用函数数据分析(FDA)框架将输出变量表示为光滑函数。我们将全局灵敏度技术推广到函数值响应，并对灵敏度指标进行显著性检验。我们将所提出的方法应用于气候经济学中的计算机模型。在确认先前工作的定性直觉的同时，我们能够检验输入假设及其相互作用的重要性。此外，该方法还可以识别灵敏度指标的时间动态。
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英文标题：
《Global Sensitivity and Domain-Selective Testing for Functional-Valued
  Responses: An Application to Climate Economy Models》
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作者：
Matteo Fontana, Massimo Tavoni, Simone Vantini
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最新提交年份：
2020
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要：
  Complex computational models are increasingly used by business and governments for making decisions, such as how and where to invest to transition to a low carbon world. Complexity arises with great evidence in the outputs generated by large scale models, and calls for the use of advanced Sensitivity Analysis techniques. To our knowledge, there are no methods able to perform sensitivity analysis for outputs that are more complex than scalar ones and to deal with model uncertainty using a sound statistical framework. The aim of this work is to address these two shortcomings by combining sensitivity and functional data analysis. We express output variables as smooth functions, employing a Functional Data Analysis (FDA) framework. We extend global sensitivity techniques to function-valued responses and perform significance testing over sensitivity indices. We apply the proposed methods to computer models used in climate economics. While confirming the qualitative intuitions of previous works, we are able to test the significance of input assumptions and of their interactions. Moreover, the proposed method allows to identify the time dynamics of sensitivity indices.
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PDF链接：
https://arxiv.org/pdf/2006.13850