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Importance analysis for models with correlated variables and its sparse grid solution
16 references·0 citing·33 second hop
Importance analysis for models with correlated variables and its sparse grid solution (2013)Importance analysis for models with corre…Variance-based sensitivity indices for models with dependent inputs (2011)Variance-based sensitivity in…Estimation of global sensitivity indices for models with dependent variables (2011)Estimation of global sensitiv…An innovating analysis of the Nataf transformation from the copula viewpoint (2008)An innovating analysis of the…Uncertainty and sensitivity analysis for models with correlated parameters (2007)Uncertainty and sensitivity a…A new uncertainty importance measure (2006)A new uncertainty importance …Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models (2005)Sensitivity Analysis in Pract…Relative Entropy Based Method for Probabilistic Sensitivity Analysis in Engineering Design (2005)Relative Entropy Based Method…High-Order Collocation Methods for Differential Equations with Random Inputs (2005)High-Order Collocation Method…Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models (2004)Sensitivity Analysis in Pract…Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)Latin hypercube sampling and …Sensitivity Analysis for Importance Assessment (2002)Sensitivity Analysis for Impo…Making best use of model evaluations to compute sensitivity indices (2002)Making best use of model eval…Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)Global sensitivity indices fo…Non-parametric statistics in sensitivity analysis for model output: A comparison of selected techniques (1990)Non-parametric statistics in …A Robust Measure of Uncertainty Importance for Use in Fault Tree System Analysis (1990)A Robust Measure of Uncertain…Probability and statistics in civil engineering (1986)Probability and statistics in…Factorial Sampling Plans for Preliminary Computational Experiments (1991)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)A simple and efficient method for global sensitivity analysis based on cumulative distribution functions (2015)Variable importance analysis: A comprehensive review (2015)The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020)Statistics for Experimenters (1979)Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences (1994)A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output (1999)“Opening Up” and “Closing Down” (2007)Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices (2019)Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)A General Probabilistic Framework for uncertainty and global sensitivity analysis of deterministic models: A hydrological case study (2013)Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters (2014)The Complexities of Agent-Based Modeling Output Analysis (2015)Large Sample Properties of Simulations Using Latin Hypercube Sampling (1987)Stochastic Finite Elements: A Spectral Approach (1991)The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data (2007)A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data (2008)Adaptive sparse polynomial chaos expansion based on least angle regression (2010)Empirical Model Building and Response Surfaces (1988)Design and Analysis of Computer Experiments (1989)Survey of sampling-based methods for uncertainty and sensitivity analysis (2006)A Review on Global Sensitivity Analysis Methods (2015)Handbook on Constructing Composite Indicators (2005)Global sensitivity analysis using polynomial chaos expansions (2007)An effective screening design for sensitivity analysis of large models (2007)Handbook on Constructing Composite Indicators: Methodology and User Guide (2008)SALib: An open-source Python library for Sensitivity Analysis (2017)Tools for Composite Indicators Building (2005)Reliability Sensitivity Algorithm Based on Stratified Importance Sampling Method for Multiple Failure Modes Systems (2010)A new algorithm for variance based importance analysis of models with correlated inputs (2012)Particle Swarm Optimization Based Approach for Estimation of Costs and Duration of Construction Projects (2020)
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References

  • 2001Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates6,377 cited
  • 2003Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems2,707 cited
  • 2002Making best use of model evaluations to compute sensitivity indices2,225 cited
  • 2005Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models1,843 cited
  • 2004Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models1,797 cited
  • 2005High-Order Collocation Methods for Differential Equations with Random Inputs1,522 cited
  • 2002Sensitivity Analysis for Importance Assessment1,303 cited
  • 2006A new uncertainty importance measure1,030 cited
  • 2011Estimation of global sensitivity indices for models with dependent variables347 cited
  • 1986Probability and statistics in civil engineering308 cited
  • 2008An innovating analysis of the Nataf transformation from the copula viewpoint286 cited
  • 2007Uncertainty and sensitivity analysis for models with correlated parameters271 cited
  • 2005Relative Entropy Based Method for Probabilistic Sensitivity Analysis in Engineering Design269 cited
  • 1990A Robust Measure of Uncertainty Importance for Use in Fault Tree System Analysis264 cited

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1990
Non-parametric statistics in sensitivity analysis for model output: A comparison of selected techniques
258 cited
  • 2011Variance-based sensitivity indices for models with dependent inputs246 cited
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