D. A. Alvarez, Reduction of uncertainty using sensitivity analysis methods for innite random sets of indexable type, International journal of approximate reasoning, vol.50, issue.5, p.750762, 2009.

B. Ankenman, B. L. Nelson, and J. Staum, Stochastic kriging for simulation metamodeling, 2008 Winter Simulation Conference, p.362370, 2008.

J. D. Betancourt, F. Bachoc, T. Klein, D. Idier, R. Pedreros et al., Gaussian process metamodeling of functional-input code for coastal ood hazard assessment, Reliability Engineering and System Safety, 2020.

J. Bigot and T. Klein, Consistent estimation of a population barycenter in the Wasserstein space, 2012.

S. Bobkov and M. Ledoux, One-dimensional empirical measures, order statistics, and kantorovich transport distances. Memoirs of the, 2019.

E. Borgonovo, A new uncertainty importance measure, Reliability Engineering & System Safety, vol.92, issue.6, p.771784, 2007.

E. Borgonovo, W. Castaings, and S. Tarantola, Moment independent importance measures: New results and analytical test cases, Risk Analysis, vol.31, issue.3, p.404428, 2011.
URL : https://hal.archives-ouvertes.fr/halsde-00683555

E. Borgonovo and B. Iooss, Moment Independent Importance Measures and a Common Rationale

T. Browne, B. Iooss, L. L. Gratiet, J. Lonchampt, and E. Remy, Stochastic simulators based optimization by gaussian process metamodels -application to maintenance investments planning issues, Quality and Reliability Engineering International, vol.32, p.20672080, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01198463

D. Bursztyn and D. M. Steinberg, Screening experiments for dispersion eects, Screening, p.2147, 2006.

S. Cambanis, G. Simons, and W. Stout, Inequalities for Ek(X, Y ) when the marginals are xed, Z. Wahrscheinlichkeitstheorie und Verw. Gebiete, vol.36, issue.4, p.285294, 1976.

V. Chabridon, Reliability-oriented sensitivity analysis under probabilistic model uncertainty, 2018.
URL : https://hal.archives-ouvertes.fr/tel-02087860

V. Chabridon, M. Balesdent, J. Bourinet, J. Morio, and N. Gayton, Evaluation of failure probability under parameter epistemic uncertainty: application to aerospace system reliability assessment, Aerospace Science and Technology, vol.69, p.526537, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01632784

S. Chatterjee, A new coecient of correlation. arXiv e-prints, 2019.

S. D. Veiga, Global sensitivity analysis with dependence measures, J. Stat. Comput. Simul, vol.85, issue.7, p.12831305, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00903283

E. De-rocquigny, N. Devictor, and S. Tarantola, Uncertainty in industrial practice, 2008.

G. Dellino and C. Meloni, Uncertainty management in simulation-optimization of complex systems, 2015.

J. Fontbona, H. Guérin, and S. Méléard, Measurability of optimal transportation and strong coupling of martingale measures, Electron. Commun. Probab, vol.15, p.124133, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00379081

J. Fort, T. Klein, and N. Rachdi, New sensitivity analysis subordinated to a contrast, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00821308

J. Fort, T. Klein, and N. Rachdi, New sensitivity analysis subordinated to a contrast, Comm. Statist. Theory Methods, vol.45, issue.15, p.43494364, 2016.
URL : https://hal.archives-ouvertes.fr/hal-00821308

R. Fraiman, F. Gamboa, and L. Moreno, Sensitivity indices for output on a Riemannian manifold. arXiv e-prints, 2018.

M. Fréchet, Les éléments aléatoires de nature quelconque dans un espace distancié, Ann. Inst. H.Poincaré, Sect. B, Prob. et Stat, vol.10, p.235310, 1948.

F. Gamboa, P. Gremaud, T. Klein, and A. Lagnoux, Global Sensitivity Analysis: a new generation of mighty estimators based on rank statistics, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02474902

F. Gamboa, A. Janon, T. Klein, and A. Lagnoux, Sensitivity analysis for multidimensional and functional outputs, Electronic Journal of Statistics, vol.8, p.575603, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00881112

F. Gamboa, A. Janon, T. Klein, A. Lagnoux, and C. Prieur, Statistical inference for Sobol pick-freeze
URL : https://hal.archives-ouvertes.fr/hal-00804668

, Monte Carlo method. Statistics, vol.50, issue.4, p.881902, 2016.

F. Gamboa, T. Klein, and A. Lagnoux, Sensitivity analysis based on Cramérvon Mises distance

. Siam/asa, J. Uncertain. Quantif, vol.6, issue.2, p.522548, 2018.

F. Gamboa, T. Klein, A. Lagnoux, and L. Moreno, Sensitivity analysis in general metric spaces, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02044223

T. Goda, Computing the variance of a conditional expectation via non-nested monte carlo. Operations Research Letters, vol.45, pp.63-67, 2017.

A. Gretton, O. Bousquet, A. Smola, and B. Schölkopf, Measuring statistical dependence with Hilbert-Schmidt norms, International conference on algorithmic learning theory, p.6377, 2005.

J. Hart and P. A. Gremaud, Robustness of the Sobol'indices to distributional uncertainty, International Journal for Uncertainty Quantication, vol.9, issue.5, 2019.

J. L. Hart, A. Alexanderian, and P. A. Gremaud, Ecient computation of Sobol'indices for stochastic models, SIAM Journal on Scientic Computing, vol.39, issue.4, pp.1514-1530, 2017.

J. L. Hart and P. A. Gremaud, Robustness of the Sobol'indices to marginal distribution uncertainty

. Siam/asa, Journal on Uncertainty Quantication, vol.7, issue.4, p.12241244, 2019.

W. Hoeding, A class of statistics with asymptotically normal distribution, Ann. Math. Statistics, vol.19, p.293325, 1948.

D. Idier, A. Aurouet, F. Bachoc, A. Baills, J. D. Betancourt et al.,

. Véron, Toward a User-Based, Robust and Fast Running Method for Coastal Flooding Forecast, Early Warning, and Risk Prevention, Journal of Coastal Research, Special Issue, vol.95, p.1115, 2020.

B. Ioss, T. Klein, and A. Lagnoux, Sobol' sensitivity analysis for stochastic numerical codes, Proceedings of the SAMO 2016 Conference, p.4849, 2016.

A. Janon, T. Klein, A. Lagnoux, M. Nodet, and C. Prieur, Asymptotic normality and eciency of two Sobol index estimators, ESAIM: Probability and Statistics, vol.18, p.342364, 2014.

J. P. Kleijnen, Design and analysis of simulation experiments, International Workshop on Simulation, p.322, 2015.

S. Kucherenko and S. Song, Dierent numerical estimators for main eect global sensitivity indices, Reliability Engineering & System Safety, vol.165, p.222238, 2017.

M. Lamboni, H. Monod, and D. Makowski, Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models, Reliability Engineering & System Safety, vol.96, issue.4, pp.450-459, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00999840

L. and L. Gratiet, Asymptotic normality of a Sobol index estimator in gaussian process regression framework, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00828596

P. Lemaître, E. Sergienko, A. Arnaud, N. Bousquet, F. Gamboa et al., Density modicationbased reliability sensitivity analysis, Journal of Statistical Computation and Simulation, vol.85, issue.6, pp.1200-1223, 2015.

A. Marrel, B. Iooss, S. Da, M. Veiga, and . Ribatet, Global sensitivity analysis of stochastic computer models with joint metamodels, Statistics and Computing, vol.22, issue.3, p.833847, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00525489

G. Mazo, An optimal tradeo between explorations and repetitions in global sensitivity analysis for stochastic computer models, 2019.

A. Meynaoui, A. Marrel, and B. Laurent, New statistical methodology for second level global sensitivity analysis. working paper or preprint, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02019412

J. Morio, Inuence of input pdf parameters of a model on a failure probability estimation, Simulation Modelling Practice and Theory, vol.19, issue.10, p.22442255, 2011.

V. Moutoussamy, S. Nanty, and B. Pauwels, Emulators for stochastic simulation codes, CEM-RACS 2013modelling and simulation of complex systems: stochastic and deterministic approaches, vol.48, p.116155, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01011770

A. Owen, Better estimation of small Sobol'sensitivity indices, 2012.

A. Owen, Variance components and generalized Sobol' indices, SIAM/ASA Journal on Uncertainty Quantication, vol.1, issue.1, p.1941, 2013.

A. Owen, J. Dick, and S. Chen, Higher order Sobol' indices. Information and Inference, vol.3, issue.1, p.5981, 2014.

K. Pearson, On the partial correlation ratio, Containing Papers of a Mathematical and Physical Character, vol.91, issue.632, p.492498, 1915.

N. Peteilh, T. Klein, T. Druot, N. Bartoli, and R. P. Liem, Challenging Top Level Aircraft Requirements based on operations analysis and data-driven models, application to take-o performance design requirements, AIAA AVIATION 2020 FORUM, AIAA AVIATION 2020 FORUM, 2020.

A. Saltelli, K. Chan, and E. Scott, Sensitivity analysis. Wiley Series in Probability and Statistics
URL : https://hal.archives-ouvertes.fr/inria-00386559

T. J. Santner, B. Williams, and W. Notz, The Design and Analysis of Computer Experiments, 2003.

P. Smets, What is Dempster-Shafer's model. Advances in the Dempster-Shafer theory of evidence, p.534, 1994.

I. Sobol, Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates, Mathematics and Computers in Simulation, vol.55, issue.1-3, p.271280, 2001.

I. M. Sobol, Sensitivity estimates for nonlinear mathematical models, Math. Modeling Comput. Experiment, vol.1, issue.4, p.407414, 1993.

B. Sudret, Global sensitivity analysis using polynomial chaos expansions, Reliability Engineering & System Safety, vol.93, issue.7, p.964979, 2008.
URL : https://hal.archives-ouvertes.fr/hal-01432217

A. W. Van-der and . Vaart, of Cambridge Series in Statistical and Probabilistic Mathematics, vol.3, 1998.

C. Villani, Topics in Optimal Transportation, 2003.