G. E. Box and N. R. Draper, Empirical model-building and response surfaces, 1987.

W. Chen, R. Jin, and A. Sudjianto, Analytical variance-based global sensitivity analysis in simulation-based design under uncertainty, TRANSACTIONS-AMERICAN SOCIETY OF MECHANICAL ENGINEERS JOURNAL OF MECHANICAL DESIGN, vol.127, issue.5, p.875, 2005.

R. Cukier, K. Levine, and . Shuler, Nonlinear sensitivity analysis of multiparameter model systems, Journal of Computational Physics, vol.26, issue.1, pp.1-42, 1978.
DOI : 10.1016/0021-9991(78)90097-9

S. , D. Veiga, and F. Gamboa, Efficient estimation of sensitivity indices, Journal of Nonparametric Statistics, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00266110

M. Garrett and . Dancik, mlegp: Maximum Likelihood Estimates of Gaussian Processes, 2011.

T. Hayfield and J. S. Racine, Nonparametric econometrics: The np package, Journal of Statistical Software, vol.27, issue.5, 2008.

J. C. Helton, J. D. Johnson, C. J. Sallaberry, and C. B. Storlie, Survey of sampling-based methods for uncertainty and sensitivity analysis, Reliability Engineering & System Safety, vol.91, issue.10-11, pp.10-111175, 2006.
DOI : 10.1016/j.ress.2005.11.017

T. Homma and A. Saltelli, 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

T. Ishigami and T. Homma, An importance quantification technique in uncertainty analysis for computer models, [1990] Proceedings. First International Symposium on Uncertainty Modeling and Analysis, pp.398-403, 1990.
DOI : 10.1109/ISUMA.1990.151285

A. Janon, M. Nodet, and C. Prieur, Certified reduced-basis solutions of viscous Burgers equation parametrized by initial and boundary values, ESAIM: Mathematical Modelling and Numerical Analysis, vol.47, issue.2, 2010.
DOI : 10.1051/m2an/2012029
URL : https://hal.archives-ouvertes.fr/inria-00524727

A. Janon, M. Nodet, and C. Prieur, UNCERTAINTIES ASSESSMENT IN GLOBAL SENSITIVITY INDICES ESTIMATION FROM METAMODELS, International Journal for Uncertainty Quantification, vol.4, issue.1, 2011.
DOI : 10.1615/Int.J.UncertaintyQuantification.2012004291
URL : https://hal.archives-ouvertes.fr/inria-00567977

W. Kahan, Pracniques: further remarks on reducing truncation errors, Communications of the ACM, vol.8, issue.1, p.40, 1965.
DOI : 10.1145/363707.363723

W. Madych and S. Nelson, Bounds on multivariate polynomials and exponential error estimates for multiquadric interpolation, Journal of Approximation Theory, vol.70, issue.1, pp.94-114, 1992.
DOI : 10.1016/0021-9045(92)90058-V

A. Marrel, B. Iooss, B. Laurent, and O. Roustant, Calculations of Sobol indices for the Gaussian process metamodel, Reliability Engineering & System Safety, vol.94, issue.3, pp.742-751, 2009.
DOI : 10.1016/j.ress.2008.07.008
URL : https://hal.archives-ouvertes.fr/hal-00239494

H. Monod, C. Naud, and D. Makowski, Uncertainty and sensitivity analysis for crop models, Working with Dynamic Crop Models: Evaluation, Analysis, Parameterization, and Applications, pp.55-99, 2006.

I. Vlad, . Morariu, . Balaji-vasan-srinivasan, C. Vikas, R. Raykar et al., Automatic online tuning for fast gaussian summation, Advances in Neural Information Processing Systems (NIPS), 2008.

N. C. Nguyen, K. Veroy, and A. T. Patera, Certified real-time solution of parametrized partial differential equations, Handbook of Materials Modeling, pp.1523-1558, 2005.

R. Development and C. Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2011.

J. Racine, An efficient cross-validation algorithm for window width selection for nonparametric kernel regression, Communications in Statistics - Simulation and Computation, vol.26, issue.4, pp.1107-1107, 1993.
DOI : 10.1137/1110024

A. Saltelli, K. Chan, and E. M. Scott, Sensitivity analysis. Wiley Series in Probability and Statistics, 2000.
URL : https://hal.archives-ouvertes.fr/inria-00386559

A. Saltelli, S. Tarantola, F. Campolongo, and M. Ratto, Sensitivity analysis in practice: a guide to assessing scientific models, 2004.
DOI : 10.1002/0470870958

T. J. Santner, B. Williams, and W. Notz, The Design and Analysis of Computer Experiments, 2003.
DOI : 10.1007/978-1-4757-3799-8

R. Schaback, Mathematical results concerning kernel techniques, Prep. 13th IFAC Symposium on System Identification, pp.1814-1819, 2003.

M. Scheuerer, R. Schaback, and M. Schlather, Interpolation of spatial data ? a stochastic or a deterministic problem ? Preprint, 2011.

I. M. Sobol, Sensitivity estimates for nonlinear mathematical models, Math. Modeling Comput. Experiment, vol.1, issue.4, pp.407-414, 1993.

I. M. Sobol, 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

C. B. Storlie, L. P. Swiler, J. C. Helton, and C. J. Sallaberry, Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models. 22, TITLE WILL BE SET BY THE PUBLISHER Reliability Engineering & System Safety, issue.11, pp.941735-1763, 2009.

B. Sudret, Global sensitivity analysis using polynomial chaos expansions, Reliability Engineering & System Safety, vol.93, issue.7, pp.964-979, 2008.
DOI : 10.1016/j.ress.2007.04.002
URL : https://hal.archives-ouvertes.fr/hal-01432217

J. Y. Tissot and C. Prieur, A bias correction method for the estimation of sensitivity indices based on random balance designs. Reliability engineering and systems safety, 2010.

A. W. Van and . Vaart, Asymptotic statistics, volume 3 of Cambridge Series in Statistical and Probabilistic Mathematics, 1998.