Design of Experiment in Global Sensitivity Analysis Based on ANOVA High-Dimensional Model Representation

DOI10.1080/03610918.2010.484122zbMath1195.62115OpenAlexW2037326939MaRDI QIDQ3589996

Xue-Ping Chen, Ying-Shan Zhang, Yin-cai Tang, Xiao-Di Wang

Publication date: 17 September 2010

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2010.484122

zbMATH Keywords

Monte Carlo algorithmglobal sensitivity analysisorthogonal decompositiondesign of experimentglobal sensitivity indices

Mathematics Subject Classification ID

Related Items (3)

Symmetrical Design of Experiment in Global Sensitivity Analysis Based on ANOVA High-dimensional Model Representation ⋮ A screening approach for non-parametric global sensitivity analysis ⋮ Orthogonal Arrays for the Estimation of Global Sensitivity Indices Based on ANOVA High-Dimensional Model Representation

Cites Work

• The jackknife estimate of variance
• The effective dimension and quasi-Monte Carlo integration
• Differentiability of the remainder term in Taylor's formula
• Differentiable even functions
• Latin supercube sampling for very high-dimensional simulations
• Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
• Unnamed Item

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