A New Importance Measure for Sensitivity Analysis

Toshimitsu Homma

88 citations0/0 files storedJournal of Nuclear Science and Technology

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Probabilistic and Robust Engineering Design Geotechnical Engineering and Analysis Risk and Safety Analysis

Abstract

Uncertainty is an integral part of risk assessment of complex engineering systems, such as nuclear power plants and space crafts. The aim of sensitivity analysis is toidentify the contribution of the uncertainty in model inputs to the uncertainty in the model output. In this study, a new importance measure that characterizes the influence of the entire input distribution on the entire output distribution was proposed. It represents the expected deviation of the cumulative distribution function (CDF) of the model output that would be obtained when one input parameter of interest were known. The applicability of this importance measure was tested with two models, a nonlinear nonmonotonic mathematical model and a risk model. In addition, a comparison of this new importance measure with several other importance measures was carried out and the differences between these measures were explained.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 88 citations exist upstream.

References

2000A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code 1979A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code 1979Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code 1981On The Quantitative Definition of Risk 1999A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output 2002Making best use of model evaluations to compute sensitivity indices 1996Importance measures in global sensitivity analysis of nonlinear models 2005Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models

Cited by

2017Extending Morris method for qualitative global sensitivity analysis of models with dependent inputs 2018An efficient method for moment-independent global sensitivity analysis by dimensional reduction technique and principle of maximum entropy 2017Structural reliability sensitivity analysis based on classification of model output 2017Multivariate global sensitivity analysis for dynamic models based on wavelet analysis

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.