Motivated by an application in the realm of climate change economics,we develop and prove the mathematical properties of a Global Sensitivity analysis (GSA) method for function-valued responses and, by exploiting the similarity between GSA and functional analysis of variance models, we employ a domain-selective nonparametric testing technique to assess the significance of the calculated sensitivity indices. The results confirm the qualitative intuitions of previous works,determining in a quantitative way the sparsity and heterogeneity of input effects and the weak impact of interactions between inputs. Moreover, we can appreciate thecomplex and non-linear time-dynamics of input effects.

Functional Data Analysis-based sensitivity analysis of Integrated Assessment Models for Climate Change Modelling

Matteo Fontana;Massimo Tavoni;Simone Vantini
In corso di stampa

Abstract

Motivated by an application in the realm of climate change economics,we develop and prove the mathematical properties of a Global Sensitivity analysis (GSA) method for function-valued responses and, by exploiting the similarity between GSA and functional analysis of variance models, we employ a domain-selective nonparametric testing technique to assess the significance of the calculated sensitivity indices. The results confirm the qualitative intuitions of previous works,determining in a quantitative way the sparsity and heterogeneity of input effects and the weak impact of interactions between inputs. Moreover, we can appreciate thecomplex and non-linear time-dynamics of input effects.
Smart Statistics for Smart Applications - Books of Short Papers SIS2019
9788891915108
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1102920
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