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International Journal for Uncertainty Quantification

年間 6 号発行

ISSN 印刷: 2152-5080

ISSN オンライン: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

GLOBAL SENSITIVITY ANALYSIS: AN EFFICIENT NUMERICAL METHOD FOR APPROXIMATING THE TOTAL SENSITIVITY INDEX

巻 6, 発行 1, 2016, pp. 1-17
DOI: 10.1615/Int.J.UncertaintyQuantification.2016012354
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要約

Variance-based sensitivity analysis and multivariate sensitivity analysis aim to apportion the variability of model output(s) into input factors and their interactions. Total sensitivity index (TSI) gives for each input its overall contribution, including the effects of its interactions with all the other inputs, in the variability of the model output(s). We investigate a numerical approximation of TSIs mainly based upon quadrature rules and quasi-Monte Carlo. The estimation of a TSI relies on the estimation of a total effect function (TEF), which allows for computing the TSI values by taking its variance. First, the paper derives the specific formula for the computation of the TEF, including the theoretical properties of the approximation, and second, it gives an overview of its application in many situations. Our approach gives the exact estimation of TSIs for a class of exact quadrature rules (especially for polynomial functions) and an interesting approximation for other functions. Numerical tests show the faster convergence rate of our approach and their usefulness in practice.

によって引用された
  1. Lamboni Matieyendou, Global sensitivity analysis: a generalized, unbiased and optimal estimator of total-effect variance, Statistical Papers, 59, 1, 2018. Crossref

  2. Lamboni Matieyendou, Multivariate sensitivity analysis: Minimum variance unbiased estimators of the first-order and total-effect covariance matrices, Reliability Engineering & System Safety, 187, 2019. Crossref

  3. Chen Xin, Molina-Cristóbal Arturo, Guenov Marin D., Riaz Atif, Efficient method for variance-based sensitivity analysis, Reliability Engineering & System Safety, 181, 2019. Crossref

  4. Lamboni Matieyendou, Uncertainty quantification: a minimum variance unbiased (joint) estimator of the non-normalized Sobol’ indices, Statistical Papers, 61, 5, 2020. Crossref

  5. Lamboni Matieyendou, Derivative-based generalized sensitivity indices and Sobol’ indices, Mathematics and Computers in Simulation, 170, 2020. Crossref

  6. Lamboni Matieyendou, Derivative-based integral equalities and inequality: A proxy-measure for sensitivity analysis, Mathematics and Computers in Simulation, 179, 2021. Crossref

  7. Lamboni Matieyendou, Kucherenko Sergei, Multivariate sensitivity analysis and derivative-based global sensitivity measures with dependent variables, Reliability Engineering & System Safety, 212, 2021. Crossref

  8. Lamboni Matieyendou, Weak derivative-based expansion of functions: ANOVA and some inequalities, Mathematics and Computers in Simulation, 194, 2022. Crossref

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