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Multivariate Global Sensitivity Analysis Based on Distance Components Decomposition.

Sinan Xiao1, Zhenzhou Lu1, Pan Wang2

  • 1School of Aeronautics, Northwestern Polytechnical University, Xi'an, Shaanxi, China.

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|July 6, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces new global sensitivity indices for multivariate models, extending traditional methods. These indices effectively measure input variable effects across the entire output distribution using an efficient Monte Carlo approach.

Keywords:
Covariance decompositionMonte Carlo simulationdistance componentsmultivariate outputsensitivity analysis

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Area of Science:

  • Computational Mathematics
  • Statistical Modeling
  • Uncertainty Quantification

Background:

  • Traditional sensitivity analysis methods like variance-based indices have limitations in capturing full output distribution effects.
  • Covariance decomposition-based indices offer improvements but can be extended for broader applicability.

Purpose of the Study:

  • To propose a novel set of multivariate global sensitivity indices based on distance components decomposition.
  • To extend the capabilities of traditional sensitivity indices for analyzing complex model outputs.
  • To introduce an efficient computational method for calculating these new indices.

Main Methods:

  • Development of new multivariate global sensitivity indices utilizing distance components decomposition.
  • Formulation of an efficient Monte Carlo method for calculating the proposed sensitivity indices.
  • Simultaneous calculation of covariance decomposition-based sensitivity indices using the same method.

Main Results:

  • The proposed sensitivity indices effectively measure input variable influence on the entire probability distribution of multivariate model outputs.
  • The developed Monte Carlo method demonstrates efficiency and stability for index computation.
  • The new indices generalize traditional variance-based and covariance decomposition-based approaches.

Conclusions:

  • The proposed multivariate global sensitivity indices offer a powerful tool for comprehensive uncertainty quantification.
  • The efficient Monte Carlo method facilitates the practical application of these advanced sensitivity measures.
  • This work advances the field of sensitivity analysis for complex computational models.