Distribution-based sensitivity analysis from a generic input-output sample. (October 2018) - Pianosi, Francesca; Wagener, Thorsten
Abstract: In a previous paper we introduced a distribution-based method for Global Sensitivity Analysis (GSA), called PAWN, which uses cumulative distribution functions of model outputs to assess their sensitivity to the model's uncertain input factors. Over the last three years, PAWN has been employed in the environmental modelling field as a useful alternative or complement to more established variance-based methods. However, a major limitation of PAWN up to now was the need for a tailored sampling strategy to approximate the sensitivity indices. Furthermore, this strategy required three tuning parameters whose optimal choice was rather unclear. In this paper, we present an alternative approximation procedure that tackles both issues and makes PAWN applicable to a generic sample of inputs and outputs while requiring only one tuning parameter. The new implementation therefore allows the user to estimate PAWN indices as complementary metrics in multi-method GSA applications without additional computational cost. Highlights: We introduce a new approximation strategy for PAWN indices. The strategy is applicable to a generic input-output sample and uses one tuning parameter only. We demonstrate that the strategy provides robust PAWN sensitivity estimates. This approximation strategy facilitates the integration of PAWN into multi-method GSA.