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

DOI10.1080/03610918.2013.849740zbMath1357.62256OpenAlexW2008123779MaRDI QIDQ2809582

Chun Luo, Ying-Shan Zhang

Publication date: 30 May 2016

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

Full work available at URL: https://doi.org/10.1080/03610918.2013.849740

zbMATH Keywords

Monte Carlo algorithmsymmetrical global sensitivity analysissymmetrical decompositionsymmetrical design of experimentsymmetrical global sensitivity indices

Mathematics Subject Classification ID

Image analysis in multivariate analysis (62H35) Orthogonal arrays, Latin squares, Room squares (05B15) Factorial statistical designs (62K15) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (5)

Symmetrical design for symmetrical global sensitivity analysis of model output ⋮ Group symmetric Latin hypercube designs for symmetrical global sensitivity analysis ⋮ \(A\)-optimal designs for non-parametric symmetrical global sensitivity analysis ⋮ Optimal space-filling design for symmetrical global sensitivity analysis of complex black-box models ⋮ Sliced symmetrical Latin hypercube designs

Cites Work

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• Orthogonal arrays obtained by generalized difference matrices with \(g\) levels
• Group partition and systems of orthogonal idempotents
• Orthogonal Arrays for the Estimation of Global Sensitivity Indices Based on ANOVA High-Dimensional Model Representation
• Design of Experiment in Global Sensitivity Analysis Based on ANOVA High-Dimensional Model Representation
• Latin supercube sampling for very high-dimensional simulations
• Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates

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