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Borgonovo E, Hazen GB, Plischke E. A Common Rationale for Global Sensitivity Measures and Their Estimation. Risk Anal. 2016;36(10):1871-1895doi: 10.1111/risa.12555.
Borgonovo, E., Hazen, G. B., & Plischke, E. (2016). A Common Rationale for Global Sensitivity Measures and Their Estimation. Risk analysis : an official publication of the Society for Risk Analysis, 36(10), 1871-1895. https://doi.org/10.1111/risa.12555
Borgonovo, Emanuele, et al. "A Common Rationale for Global Sensitivity Measures and Their Estimation." Risk analysis : an official publication of the Society for Risk Analysis vol. 36,10 (2016): 1871-1895. doi: https://doi.org/10.1111/risa.12555
Borgonovo E, Hazen GB, Plischke E. A Common Rationale for Global Sensitivity Measures and Their Estimation. Risk Anal. 2016 Oct;36(10):1871-1895. doi: 10.1111/risa.12555. Epub 2016 Feb 09. PMID: 26857789.
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Risk Anal. 2016 Oct;36(10):1871-1895. doi: 10.1111/risa.12555. Epub 2016 Feb 09.

A Common Rationale for Global Sensitivity Measures and Their Estimation.

Risk analysis : an official publication of the Society for Risk Analysis

Emanuele Borgonovo, Gordon B Hazen, Elmar Plischke

Affiliations

  1. Department of Decision Sciences, Bocconi University, Milan, Italy.
  2. Department of Industrial Engineering and Management Science and Engineering, Northwestern University, Evanston, IL, USA.
  3. Clausthal University of Technology, Clausthal-Zellerfeld, Germany.

PMID: 26857789 DOI: 10.1111/risa.12555

Abstract

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.

© 2016 Society for Risk Analysis.

Keywords: Global sensitivity measures; Monte Carlo simulation; probabilistic sensitivity analysis; risk analysis; uncertainty analysis

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