Benchmarking the PAWN Distribution-based Method Against the Variance-based Method in Global Sensitivity Analysis: Empirical Results

Esteve Borràs mora, James Spelling, Adriaan Van Der Weijde

Research output: Contribution to journalArticlepeer-review

Abstract

The search for new and more efficient global sensitivity analysis methods has led to the development of the PAWN distribution-based method. This method has been proven to overcome one of the main limitation of variance-based methods – the moment independent property. In this regard, the distribution-based method has outperformed the variance-based method for some highly-skewed or multi-modal distributions. However, despite its increasing popularity, there is a lack of understanding about the performance and properties of the distribution-based method. The benchmark presented in this paper is an attempt to remedy this. We compare the distribution- based method against the variance-based method for a set of well-known test functions. We show that, whereas the distribution-based method can be used as a complementary approach to variance-based methods, which is especially useful when dealing with highly-skewed or multi-modal distributions, it fails to rank different inputs that have different orders of magnitude in their contribution of the response.
Original languageEnglish
Article number104556
JournalEnvironmental Modelling and Software
Volume122
Early online date17 Oct 2019
DOIs
Publication statusE-pub ahead of print - 17 Oct 2019

Keywords

  • Global sensitivity analysis
  • PAWN distribution-based method
  • variance-based sensitivity analysis

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