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A Common Rationale for Global Sensitivity Measures and Their Estimation.

Emanuele Borgonovo1, Gordon B Hazen2, Elmar Plischke3

  • 1Department of Decision Sciences, Bocconi University, Milan, Italy.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|February 10, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a unified, efficient single-loop Monte Carlo method for estimating sensitivity and uncertainty measures in risk analysis. The method is more general and less restrictive than previous approaches.

Keywords:
Global sensitivity measuresMonte Carlo simulationprobabilistic sensitivity analysisrisk analysisuncertainty analysis

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Area of Science:

  • Risk Analysis
  • Computational Statistics
  • Uncertainty Quantification

Background:

  • Sensitivity and uncertainty measures are crucial in risk analysis.
  • Traditional Monte Carlo estimation for these measures often uses an inefficient double-loop structure.
  • Prior work demonstrated single-loop consistency for specific measures like variance, density, and information value.

Purpose of the Study:

  • To provide a unified proof of single-loop Monte Carlo consistency for a broad class of sensitivity and uncertainty measures.
  • To develop a more general and less restrictive estimation procedure.
  • To demonstrate the applicability and numerical convergence of the proposed method.

Main Methods:

  • Developed a unified theoretical framework for single-loop Monte Carlo consistency.
  • The proof accommodates correlations among model inputs and categorical variables.
  • Examined numerical convergence across various sensitivity measures.

Main Results:

  • Established single-loop consistency for any measure satisfying a common rationale, under less restrictive assumptions.
  • The unified proof is more general than previous individual proofs.
  • Demonstrated numerical convergence and applied the method to a medical case study.

Conclusions:

  • The single-loop Monte Carlo procedure offers a more efficient and broadly applicable method for sensitivity and uncertainty analysis in risk assessment.
  • This unified approach simplifies and enhances the estimation of complex probabilistic measures.
  • The method's robustness is confirmed through numerical convergence and a real-world case study application.