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Two-Stage Sensitivity-Based Group Screening in Computer Experiments
16 references·5 citing·29 second hop
Two-Stage Sensitivity-Based Group Screening in Computer Experiments (2012)Two-Stage Sensitivity-Based Group Screeni…Choosing the Sample Size of a Computer Experiment: A Practical Guide (2009)Choosing the Sample Size of a…Blind Kriging: A New Method for Developing Metamodels (2008)Blind Kriging: A New Method f…Variable Selection for Gaussian Process Models in Computer Experiments (2006)Variable Selection for Gaussi…Screening: Methods for Experimentation in Industry, Drug Discovery, and Genetics (2006)Screening: Methods for Experi…Design and Modeling for Computer Experiments (2005)Design and Modeling for Compu…Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach (2004)Probabilistic Sensitivity Ana…Combining Field Data and Computer Simulations for Calibration and Prediction (2004)Combining Field Data and Comp…The Design and Analysis of Computer Experiments (2003)The Design and Analysis of Co…A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)A Comparison of Three Methods…On the use of variance reducing multipliers in Monte Carlo computations of a global sensitivity index (1999)On the use of variance reduci…Importance measures in global sensitivity analysis of nonlinear models (1996)Importance measures in global…Screening, Predicting, and Computer Experiments (1992)Screening, Predicting, and Co…Minimax and maximin distance designs (1990)Minimax and maximin distance …Design and Analysis of Computer Experiments (1989)Design and Analysis of Comput…Deterministic uncertainty analysis (1987)Deterministic uncertainty ana…A Study of the Group Screening Method (1961)A Study of the Group Screenin…PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling (2023)PyApprox: A software package …On Design Orthogonality, Maximin Distance, and Projection Uniformity for Computer Experiments (2020)On Design Orthogonality, Maxi…Design of Experiments for Screening (2015)Design of Experiments for Scr…Factor screening for simulation with multiple responses: Sequential bifurcation (2014)Factor screening for simulati…Algorithms for Generating Maximin Latin Hypercube and Orthogonal Designs (2011)Algorithms for Generating Max…Statistics for Experimenters (1979)Handbook on Constructing Composite Indicators (2005)An effective screening design for sensitivity analysis of large models (2007)Sensitivity analysis of environmental models: A systematic review with practical workflow (2016)A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output (1999)A new uncertainty importance measure (2006)Bayesian analysis of computer code outputs: A tutorial (2006)How to avoid a perfunctory sensitivity analysis (2010)THE DESIGN OF OPTIMUM MULTIFACTORIAL EXPERIMENTS (1946)A methodology for fitting and validating metamodels in simulation1Two anonymous referees' comments on the first draft lead to an improved organization of our paper.1 (2000)Reliability-based design optimization using kriging surrogates and subset simulation (2011)DiceKriging , DiceOptim : Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization (2012)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (1979)Regression and Kriging metamodels with their experimental designs in simulation: A review (2016)Design and Analysis of Simulation Experiments (2018)Stochastic Kriging for Simulation Metamodeling (2009)A Review on Global Sensitivity Analysis Methods (2015)The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020)Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)Taking the Human Out of the Loop: A Review of Bayesian Optimization (2015)A review of techniques for parameter sensitivity analysis of environmental models (1994)Exploratory designs for computational experiments (1995)An efficient algorithm for constructing optimal design of computer experiments (2004)Response Surface Methodology: A Retrospective and Literature Survey (2004)A Framework for Validation of Computer Models (2007)Quantification of Model Uncertainty: Calibration, Model Discrepancy, and Identifiability (2012)An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach (2011)Sensitivity analysis: A review of recent advances (2015)
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References

  • 2000A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code8,233 cited
  • 1989Design and Analysis of Computer Experiments6,958 cited
  • 2003The Design and Analysis of Computer Experiments2,204 cited
  • 1996Importance measures in global sensitivity analysis of nonlinear models2,014 cited
  • 1990Minimax and maximin distance designs1,452 cited
  • 2005Design and Modeling for Computer Experiments1,135 cited
  • 2004Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach1,085 cited
  • 2009Choosing the Sample Size of a Computer Experiment: A Practical Guide766 cited
  • 1992Screening, Predicting, and Computer Experiments633 cited
  • 2004Combining Field Data and Computer Simulations for Calibration and Prediction622 cited
  • 2008Blind Kriging: A New Method for Developing Metamodels264 cited
  • 2006Variable Selection for Gaussian Process Models in Computer Experiments167 cited
  • 1961A Study of the Group Screening Method147 cited
  • 1999On the use of variance reducing multipliers in Monte Carlo computations of a global sensitivity index114 cited
  • 2006Screening: Methods for Experimentation in Industry, Drug Discovery, and Genetics

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88 cited
  • 1987Deterministic uncertainty analysis66 cited
  • Cited by

    • 2020On Design Orthogonality, Maximin Distance, and Projection Uniformity for Computer Experiments54 cited
    • 2015Design of Experiments for Screening47 cited
    • 2014Factor screening for simulation with multiple responses: Sequential bifurcation42 cited
    • 2011Algorithms for Generating Maximin Latin Hypercube and Orthogonal Designs42 cited
    • 2023PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling19 cited