Orthogonal Arrays for the Estimation of Global Sensitivity Indices Based on ANOVA High-Dimensional Model Representation

DOI10.1080/03610918.2011.575500zbMath1416.62420OpenAlexW1969114335MaRDI QIDQ3102883

Ying-Shan Zhang, Yin-cai Tang, Xiao-Di Wang

Publication date: 25 November 2011

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

Full work available at URL: https://doi.org/10.1080/03610918.2011.575500

zbMATH Keywords

Monte Carlo simulationorthogonal arrayquasi-Monte Carloglobal sensitivity indices

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (2)

Further results on orthogonal arrays for the estimation of global sensitivity indices based on alias matrix ⋮ Symmetrical Design of Experiment in Global Sensitivity Analysis Based on ANOVA High-dimensional Model Representation

Cites Work

• Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index
• On the construction of asymmetrical orthogonal arrays
• Making best use of model evaluations to compute sensitivity indices
• Analysis of variance designs for model output
• The effective dimension and quasi-Monte Carlo integration
• Design of Experiment in Global Sensitivity Analysis Based on ANOVA High-Dimensional Model Representation
• Latin supercube sampling for very high-dimensional simulations
• Sensitivity Analysis in Practice
• Monte Carlo estimators for small sensitivity indices
• On the distribution of points in a cube and the approximate evaluation of integrals
• Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates

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