A Common Rationale for Global Sensitivity Measures and Their Estimation

Emanuele Borgonovo*, Gordon B. Hazen, Elmar Plischke

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

80 Scopus citations


Measures of sensitivity and uncertainty have become an integral part of risk analysis. Many such measures have a conditional probabilistic structure, for which a straightforward Monte Carlo estimation procedure has a double-loop form. Recently, a more efficient single-loop procedure has been introduced, and consistency of this procedure has been demonstrated separately for particular measures, such as those based on variance, density, and information value. In this work, we give a unified proof of single-loop consistency that applies to any measure satisfying a common rationale. This proof is not only more general but invokes less restrictive assumptions than heretofore in the literature, allowing for the presence of correlations among model inputs and of categorical variables. We examine numerical convergence of such an estimator under a variety of sensitivity measures. We also examine its application to a published medical case study.

Original languageEnglish (US)
Pages (from-to)1871-1895
Number of pages25
JournalRisk Analysis
Issue number10
StatePublished - Oct 1 2016


  • Global sensitivity measures
  • Monte Carlo simulation
  • probabilistic sensitivity analysis
  • risk analysis
  • uncertainty analysis

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Physiology (medical)


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