Sample-based estimation of correlation ratio with polynomial approximation

Authors:
Daniel Lewandowski;Roger M. Cooke;Radboud J. Duintjer Tebbens
Affiliations:
Delft University of Technology, Delft, The Netherlands;Delft University of Technology and Resources For The Future, Delft, The Netherlands;Harvard School of Public Health, Boston, MA
Venue:
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Year:
2007

Citing 4
Cited 2

Applied regression analysis and other multivariable methods

Applied regression analysis and other multivariable methods
Sensitivity analysis of model output: variance-based methods make the difference

Proceedings of the 29th conference on Winter simulation
A quantitative model-independent method for global sensitivity analysis of model output

Technometrics
Winding Stairs: A sampling tool to compute sensitivity indices

Statistics and Computing

Ethylene: composants dynamiques pour la mise en œuvre d'IHM plastiques en informatique ambiante

Proceedings of the 21st International Conference on Association Francophone d'Interaction Homme-Machine
Feature analysis and classification of lymph nodes

ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

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.