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Data Acquisition Protocol for Determining Embedded Sensitivity Functions
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Moment-independent sensitivity analysis using copula.

Pengfei Wei1, Zhenzhou Lu, Jingwen Song

  • 1Northwestern Polytechnical University, Xi'an, Shaanxi, 710072, China.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|September 13, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces new moment-independent sensitivity analysis (SA) indices to measure model uncertainty. The proposed methods efficiently estimate these indices, reducing computational load in risk assessment.

Keywords:
Copula densitydependence measureempirical copula functionmoment-independent sensitivity analysisrisk assessment

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Area of Science:

  • Quantitative Risk Assessment
  • Computational Statistics

Background:

  • Model uncertainty is a key challenge in risk assessment.
  • Moment-independent sensitivity analysis (SA) is crucial for quantifying input importance.

Purpose of the Study:

  • To interpret the original moment-independent SA index (delta index) as a dependence measure.
  • To introduce a new moment-independent SA index (extended delta index) using copula.
  • To propose nonparametric estimation methods for these indices.

Main Methods:

  • Interpreting the delta index as a dependence measure.
  • Developing an extended delta index based on copula theory.
  • Implementing nonparametric estimation techniques for both indices.
  • Addressing the curse of dimensionality with sample-based computation.

Main Results:

  • The delta and extended delta indices yield identical importance rankings across test cases.
  • The proposed nonparametric methods significantly reduce computational burden.
  • The methods are validated on an analytical example, a risk assessment model, and the levelE model.

Conclusions:

  • The extended delta index offers a valuable tool for model uncertainty analysis.
  • Nonparametric estimation methods provide an efficient and scalable approach to SA.
  • These advancements enhance the practical application of SA in risk assessment.