Article2007

Sample-based estimation of correlation ratio with polynomial approximation

Roger M. Cooke · Radboud J. Duintjer Tebbens

38 citations0/0 files storedACM Transactions on Modeling and Computer Simulation

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Probabilistic and Robust Engineering Design Simulation Techniques and Applications Gaussian Processes and Bayesian Inference

Abstract

Sensitivity analysis has become a natural step in the uncertainty analysis framework. As there is no general sensitivity measure that would capture all information on impact of input factors on model output, analysts tend to combine various measures to obtain a broader image of interactions between different modes. This article concentrates on the correlation ratio, demonstrates methods for calculating this quantity efficiently and accurately, and compares the results. A new method inspired by artificial intelligence techniques emerges as outperforming the familiar methods.

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References

1999A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output 2004Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach 2000Sensitivity Anaysis as an Ingredient of Modeling 2000Sensitivity Analysis as an Ingredient of Modeling 1997Sensitivity analysis of model output 2002Practical Approaches To Construct RS-HDMR Component Functions 2002An importance quantification technique in uncertainty analysis for computer models 2006Random Sampling-High Dimensional Model Representation (RS-HDMR) and Orthogonality of Its Different Order Component Functions

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2016Sensitivity to Energy Technology Costs: A Multi-Model Comparison Analysis 2011An adaptive correlation ratio method using the cumulative sum of the reordered output 2011A variance-based sensitivity index function for factor prioritization 2020An effective strategy for combining variance- and distribution-based global sensitivity analysis 2016Addressing factors fixing setting from given data: A comparison of different methods

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