M. Pourgol-mohamad, M. Modarres, and A. Mosleh, Integrated methodology for thermal-hydraulic code uncertainty analysis with application, Nuclear Technology, vol.165, p.333, 2009.

A. Bucalossi, A. Petruzzi, M. Kristof, and F. D'auria, Comparison between bestestimate-plus-uncertainty methods and conservative tools for nuclear power plant licensing, Nuclear Technology, vol.172, p.29, 2010.

A. Prosek and B. Mavko, The state-of-the-art theory and applications of best-estimate plus uncertainty methods, Nuclear Technology, vol.158, p.69, 2007.

G. Wilson, Historical insights in the development of Best estimate Plus Uncertainty safety analysis, Annals of Nuclear Energy, vol.52, issue.2, 2013.

F. Sanchez-saez, A. , J. Villanueva, S. Carlos, and S. Martorell, Uncertainty analysis of large break loss of coolant accident in a pressurized water reactor using non-parametric methods, Reliability Engineering and System Safety, vol.174, p.19, 2018.

W. Nutt and G. Wallis, Evaluation of nuclear safety from the outputs of computer codes in the presence of uncertainties, Reliability Engineering and System Safety, vol.83, p.57, 2004.

G. Wallis, Uncertainties and probabilities in nuclear reactor regulation, Nuclear Engineering and Design, vol.237, p.1586, 2004.

A. De-crécy, P. Bazin, H. Glaeser, T. Skorek, J. Joucla et al., Uncertainty and sensitivity analysis of the LOFT L2-5 test: Results of the BEMUSE programme, Nuclear Engineering and Design, vol.12, p.3561, 2008.

A. Petruzzi and F. D'auria, Approaches, relevant topics, and internal method for uncertainty evaluation in predictions of thermal-hydraulic system codes, Science and Technology of Nuclear Installations, vol.17, 2008.

R. Martin and W. Nutt, Perspectives on the application of order-statistics in best-estimate plus uncertainty nuclear safety analysis, Nuclear Engineering and Design, vol.241, p.274, 2011.

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

K. Fang, R. Li, and A. Sudjianto, Design and modeling for computer experiments, 2006.

A. Forrester, A. Sobester, and A. Keane, Engineering design via surrogate modelling: a practical guide, 2008.

C. Cannamela, J. Garnier, and B. Iooss, Controlled stratification for quantile estimation, Annals of Apllied Statistics, vol.2, p.1554, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00355016

E. Zio, F. Di-maio, S. Martorell, and Y. Nebot, Neural network and Order Statistics for Quantifying nuclear power plant Safety Margins, Safety, reliability and risk analysis-Proceedings of the ESREL, 2008.

G. Lorenzo, P. Zanocco, M. Giménez, B. Mmarqù-es, R. Iooss et al., Assessment of an isolation condenser of an integral reactor in view of uncertainties in engineering parameters, Science and Technology of Nuclear Installations, vol.9, 2011.

R. Ghanem, D. Higdon, and H. Owhadi, Springer Handbook on Uncertainty Quantification, 2017.

T. Santner, B. Williams, and W. Notz, The design and analysis of computer experiments, 2003.

T. Muehlenstaedt, O. Roustant, L. Carraro, and S. Kuhnt, Data-driven Kriging models based on FANOVA-decomposition, Statistics & Computing, vol.22, p.723, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00537781

N. Durrande, D. G. Roustant, and L. Carraro, ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis, Journal of Multivariate Analysis, vol.155, p.57, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00601472

W. Welch, R. Buck, J. Sacks, H. Wynn, T. Mitchell et al., Screening, predicting, and computer experiments, Technometrics, vol.34, p.15, 1992.
DOI : 10.2307/1269548

A. Marrel, B. Iooss, F. Van-dorpe, and E. Volkova, An efficient methodology for modeling complex computer codes with Gaussian processes, Computational Statistics and Data Analysis, vol.52, p.4731, 2008.
DOI : 10.1016/j.csda.2008.03.026

URL : https://hal.archives-ouvertes.fr/hal-00239492

D. Woods and S. Lewis, Design of experiments for screening, Springer Handbook on Uncertainty Quantification, pp.1143-1185, 2017.

S. D. Veiga, Global sensitivity analysis with dependence measures, Journal of Statistical Computation and Simulation, vol.85, p.1283, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01128666

M. De-lozzo and A. Marrel, New improvements in the use of dependence measures for sensitivity analysis and screening, Journal of Statistical Computation and Simulation, vol.86, p.3038, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01090475

B. Iooss and A. Marrel, An efficient methodology for the analysis and metamodeling of computer experiments with large number of inputs, 2017.

R. Conference and . Island, , 2017.

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, p.833, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00232805

I. Zabalza, J. Dejean, and D. Collombier, Prediction and density estimation of a horizontal well productivity index using generalized linear models, ECMOR VI, 1998.

P. Mazgaj, J. Vacher, and S. Carnevali, Comparison of CATHARE results with the experimental results of cold leg intermediate break LOCA obtained during ROSA-2/LSTF test 7, EPJ Nuclear Sciences & Technology, vol.2, p.1, 2016.

M. Mckay, R. Beckman, and W. Conover, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, vol.21, p.239, 1979.

G. Damblin, M. Couplet, and B. Iooss, Numerical studies of space filling designs: Optimization of Latin hypercube samples and subprojection properties, Journal of Simulation, vol.7, p.276, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00848240

V. Joseph, E. Gul, and S. Ba, Maximum projection designs for computer experiments, Biometrika, vol.102, p.371, 2015.

R. Jin, W. Chen, and A. Sudjianto, An efficient algorithm for constructing optimal design of computer experiments, Journal of Statistical Planning and Inference, vol.134, p.268, 2005.

J. Loeppky, J. Sacks, and W. Welch, Choosing the sample size of a computer experiment: A practical guide, Technometrics, vol.51, p.366, 2009.

E. Parzen, On Estimation of a Probability Density Function and Mode, The Annals of Mathematical Statistics, vol.33, p.1065, 1962.

S. Kucherenko and B. Iooss, Derivative-Based Global Sensitivity Measures, Springer Handbook on Uncertainty Quantification, pp.1241-1263, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01079358

O. Roustant, F. Barthe, and B. Iooss, Poincaré inequalities on intervals-application to sensitivity analysis, Electronic Journal of Statistics, vol.2, p.3081, 2017.

B. Iooss and P. Lema??trelema??tre, Uncertainty management in Simulation-Optimization of Complex Systems: Algorithms and Applications, C. Meloni and G. Dellino, pp.101-122, 2015.

G. Gretton, O. Bousquet, A. Smola, and B. Schölkopf, Measuring statistical dependence with Hilbert-Schmidt norms, Proceedings Algorithmic Learning Theory, pp.63-77, 2005.

J. Sacks, W. Welch, T. Mitchell, and H. Wynn, Design and analysis of computer experiments, Statistical Science, vol.4, p.409, 1989.

C. Rasmussen and C. Williams, Gaussian processes for machine learning, 2006.

A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni et al., Global sensitivity analysis-The primer, 2008.

J. Kleijnen, Sensitivity analysis and related analyses: a review of some statistical techniques, Journal of Statistical Computation and Simulation, vol.57, p.111, 1997.

H. Frey and S. Patil, Identification and review of sensitivity analysis methods, Risk Analysis, vol.22, p.553, 2002.

J. Helton, J. Johnson, C. Salaberry, and C. Storlie, Survey of sampling-based methods for uncertainty and sensitivity analysis, Reliability Engineering and System Safety, vol.91, p.1175, 2006.

D. Cacuci, Sensitivity theory for nonlinear systems. I. Nonlinear functional analysis approach, Journal of Mathematical Physics, vol.22, p.2794, 1981.

I. Sobol, Sensitivity estimates for non linear mathematical models, Mathematical Modelling and Computational Experiments, vol.1, p.407, 1993.

T. Homma and A. Saltelli, Importance measures in global sensitivity analysis of non linear models, Reliability Engineering and System Safety, vol.52, p.1, 1996.

W. Hoeffding, A class of statistics with asymptotically normal distributions, Annals of Mathematical Statistics, vol.19, p.293, 1948.

F. Gamboa, A. Janon, T. Klein, A. Lagnoux, and C. Prieur, Statistical inference for Sobol pick freeze Monte Carlo methods, Statistics, vol.50, p.881, 2016.

J. Oakley, Estimating percentiles of uncertain computer code outputs, Applied Statistics, vol.53, p.83, 2004.

B. Rutherford, A response-modeling alternative to surrogate models for support in computational analyses, Reliability Engineering and System Safety, vol.91, p.1322, 2006.

J. Chiì-es and P. Delfiner, Geostatistics: Modeling spatial uncertainty, 1999.

L. L. Gratiet, C. Cannamela, and B. Iooss, A Bayesian approach for global sensitivity analysis of (multifidelity) computer codes, SIAM/ASA Journal on Uncertainty Quantification, vol.2, p.336, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00842432

E. Zio and F. Di-maio, Bootstrap and Order Statistics for Quantifying Thermal-Hydraulic Code Uncertainties in the Estimation of Safety Margins, Science and Technology of Nuclear Installations, vol.340164, 2008.

J. Hessling and J. Uhlmann, Robustness of Wilks' conservative estimate of confidence intervals, International Journal for Uncertainty Quantification, vol.5, p.569, 2015.