Probability Measures, The Annals of Probability, vol.27, issue.4, pp.1903-1921, 1999. ,
DOI : 10.1214/aop/1022677553
The use of graph theory in the sensitivity analysis of the model output: a second order screening method, Reliability Engineering & System Safety, vol.64, issue.1, pp.1-12, 1999. ,
DOI : 10.1016/S0951-8320(98)00008-8
An effective screening design for sensitivity analysis of large models, Environmental Modelling & Software, vol.22, issue.10, pp.1509-1518, 2007. ,
DOI : 10.1016/j.envsoft.2006.10.004
The New Morris Method: an efficient second-order screening method, Reliability Engineering & System Safety, vol.78, issue.1, pp.77-83, 2002. ,
DOI : 10.1016/S0951-8320(02)00109-6
Estimation of the Derivative-Based Global Sensitivity Measures Using a Gaussian Process Metamodel, SIAM/ASA Journal on Uncertainty Quantification, vol.4, issue.1, 2015. ,
DOI : 10.1137/15M1013377
URL : https://hal.archives-ouvertes.fr/hal-01164215
Extending Morris method: identification of the interaction graph using cycle-equitable designs, Journal of Statistical Computation and Simulation, vol.85, issue.7, pp.1398-1419, 2015. ,
DOI : 10.1016/S0951-8320(02)00109-6
Predictive learning via rule ensembles, The Annals of Applied Statistics, vol.2, issue.3, pp.916-954, 2008. ,
DOI : 10.1214/07-AOAS148
URL : http://arxiv.org/abs/0811.1679
Total interaction index: A variance-based sensitivity index for second-order interaction screening, Journal of Statistical Planning and Inference, vol.147, pp.212-223, 2014. ,
DOI : 10.1016/j.jspi.2013.11.007
URL : https://hal.archives-ouvertes.fr/hal-00631066
Evaluating derivatives: Principles and techniques of automatic differentiation, SIAM Philadelphia, 2008. ,
DOI : 10.1137/1.9780898717761
Importance measures in global sensitivity analysis of nonlinear models, Reliability Engineering & System Safety, vol.52, issue.1, pp.1-17, 1996. ,
DOI : 10.1016/0951-8320(96)00002-6
Some new insights in derivative-based global sensitivity measures, Proceedings of the PSAM11 ESREL 2012 Conference, pp.1094-1104, 2012. ,
A first look at quasi-Monte Carlo for lattice field theory problems, Journal of Physics: Conference Series, vol.454, pp.948-959, 2014. ,
DOI : 10.1088/1742-6596/454/1/012043
Analysis of variance designs for model output, Computer Physics Communications, vol.117, issue.1-2, pp.25-43, 1999. ,
DOI : 10.1016/S0010-4655(98)00154-4
Global Sensitivity Analysis Challenges in Biological Systems Modeling, Industrial & Engineering Chemistry Research, vol.48, issue.15, pp.1135-1148, 2009. ,
DOI : 10.1021/ie900139x
Derivative-Based Global Sensitivity Measures and Their Link with Sobol??? Sensitivity Indices, Proceedings of the Eleventh International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, 2014. ,
DOI : 10.1007/978-3-319-33507-0_23
URL : http://arxiv.org/abs/1605.07830
Monte Carlo evaluation of derivative-based global sensitivity measures, Reliability Engineering & System Safety, vol.94, issue.7, pp.1135-1148, 2009. ,
DOI : 10.1016/j.ress.2008.05.006
New way of estimating total sensitivity indices, Proceedings of the 7th International Conference on Sensitivity Analysis of Model Output (SAMO 2013), 2013. ,
Derivative-based global sensitivity measures: General links with Sobol??? indices and numerical tests, Mathematics and Computers in Simulation, vol.87, pp.45-54, 2013. ,
DOI : 10.1016/j.matcom.2013.02.002
URL : https://hal.archives-ouvertes.fr/hal-00666473
Estimating Mean Dimensionality of Analysis of Variance Decompositions, Journal of the American Statistical Association, vol.101, issue.474, pp.712-721, 2006. ,
DOI : 10.1198/016214505000001410
Factorial Sampling Plans for Preliminary Computational Experiments, Technometrics, vol.1, issue.2, pp.161-174, 1991. ,
DOI : 10.2307/1266468
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.584.521
Data-driven Kriging models based on FANOVA-decomposition, Statistics and Computing, vol.34, issue.4, pp.723-738, 2012. ,
DOI : 10.1007/s11222-011-9259-7
URL : https://hal.archives-ouvertes.fr/emse-00699673
Monte Carlo gradient estimation in high dimensions, International Journal for Numerical Methods in Engineering, vol.1, issue.2, pp.172-188, 2010. ,
DOI : 10.1007/s00466-006-0127-9
Global sensitivity of structural variability by random sampling, Computer Physics Communications, vol.181, issue.12, pp.2072-2081, 2010. ,
DOI : 10.1016/j.cpc.2010.08.007
Analyse de sensibilité globale du module MASCARET par l'utilisation de la différentiation automatique, 2015. ,
Simplex-based screening designs for estimating metamodels, Reliability Engineering & System Safety, vol.94, issue.7, pp.1156-1160, 2009. ,
DOI : 10.1016/j.ress.2008.08.002
URL : https://hal.archives-ouvertes.fr/hal-00305313
Novel global sensitivity analysis methodology accounting for the crucial role of the distribution of input parameters: application to systems biology models, International Journal of Robust and Nonlinear Control, vol.3, issue.10, pp.1082-1102, 2012. ,
DOI : 10.1002/rnc.2797
Crossed-derivative based sensitivity measures for interaction screening, Mathematics and Computers in Simulation, vol.105, pp.105-118, 2014. ,
DOI : 10.1016/j.matcom.2014.05.005
URL : https://hal.archives-ouvertes.fr/hal-00845446
Making best use of model evaluations to compute sensitivity indices, Computer Physics Communications, vol.145, issue.2, pp.280-297, 2002. ,
DOI : 10.1016/S0010-4655(02)00280-1
Global sensitivity analysis -The primer, 2008. ,
DOI : 10.1002/9780470725184
Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index, Computer Physics Communications, vol.181, issue.2, pp.259-270, 2010. ,
DOI : 10.1016/j.cpc.2009.09.018
Improved sensitivity through Morris extension, Chemometrics and Intelligent Laboratory Systems, vol.113, pp.52-57, 2012. ,
DOI : 10.1016/j.chemolab.2011.10.006
Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates, Mathematics and Computers in Simulation, vol.55, issue.1-3, pp.271-280, 2001. ,
DOI : 10.1016/S0378-4754(00)00270-6
On an alternative global sensitivity estimators, Proceedings of SAMO 1995, pp.40-42, 1995. ,
Global sensitivity indices for non linear mathematical models, Review . Wilmott Magazine, vol.1, pp.56-61, 2005. ,
Derivative based global sensitivity measures and their link with global sensitivity indices, Mathematics and Computers in Simulation, vol.79, issue.10, pp.3009-3017, 2009. ,
DOI : 10.1016/j.matcom.2009.01.023
A new derivative based importance criterion for groups of variables and its link with the global sensitivity indices, Computer Physics Communications, vol.181, issue.7, pp.1212-1217, 2010. ,
DOI : 10.1016/j.cpc.2010.03.006
Computing derivative-based global sensitivity measures using polynomial chaos expansions, Reliability Engineering & System Safety, vol.134, pp.241-250, 2015. ,
DOI : 10.1016/j.ress.2014.07.009
URL : https://hal.archives-ouvertes.fr/hal-01154395
Screening Method Using the Derivative-based Global Sensitivity Indices with Application to Reservoir Simulator, Oil & Gas Science and Technology ??? Revue d???IFP Energies nouvelles, vol.69, issue.4, pp.619-632, 2014. ,
DOI : 10.2516/ogst/2013195