A Generalized Kernel Method for Global Sensitivity Analysis

DOI10.1137/20M1354829OpenAlexW4206488953MaRDI QIDQ5862896

John Barr, Herschel Rabitz

Publication date: 10 March 2022

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/20m1354829

zbMATH Keywords

kernel methodsglobal sensitivity analysismultivariate outputmoment-independent sensitivity analysis

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Learning and adaptive systems in artificial intelligence (68T05) Characterization and structure theory of statistical distributions (62E10)

Cites Work

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• Equivalence of distance-based and RKHS-based statistics in hypothesis testing
• Generalized moment-independent importance measures based on Minkowski distance
• Nonlinear sensitivity analysis of multiparameter model systems
• General foundations of high-dimensional model representations
• On the empirical estimation of integral probability metrics
• Kernel dimension reduction in regression
• Sensitivity analysis for multidimensional and functional outputs
• 10.1162/153244302760185252
• The $f$-Sensitivity Index
• New sensitivity analysis subordinated to a contrast
• Hilbert space embeddings and metrics on probability measures
• A Hilbert Space Embedding for Distributions
• Integral Probability Metrics and Their Generating Classes of Functions
• Kernel Mean Embedding of Distributions: A Review and Beyond
• Global sensitivity analysis with dependence measures
• Universality, Characteristic Kernels and RKHS Embedding of Measures
• Theory of Reproducing Kernels
• Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
• Efficient implementation of high dimensional model representations

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