. [. Bibliography, G. Avellaneda, S. Davis, and . Mallat, Adaptive greedy approximations Constructive approximation A class of bivariate distributions including the bivariate logistic, Journal of Multivariate Analysis, vol.13, issue.83, pp.57-98405, 1978.

]. N. Aro09 and . Aronszajn, Theory of reproducing kernels, Transactions of the American Matrhematical Society, vol.68, issue.3, pp.337-404, 2009.

I. [. Abramowitz and . Stegun, Handbook of mathematical functions: with formulas, graphs, and mathematical tables, 1965.

]. D. Bar12 and . Barber, Bayesian reasoning and machine learning

. A. Bcdd08, A. Barron, W. Cohen, R. Dahmen, and . Devore, Approximation and learning by greedy algorithms. The Annals of Statistics, pp.64-94, 2008.

W. [. Borgonovo, S. Castaings, and . Tarantola, Moment Independent Importance Measures: New Results and Analytical Test Cases, Risk Analysis, vol.33, issue.3, pp.404-428, 2011.
DOI : 10.1111/j.1539-6924.2010.01519.x

URL : https://hal.archives-ouvertes.fr/halsde-00683555

W. [. Borgonovo, S. Castaings, and . Tarantola, Model emulation and moment-independent sensitivity analysis: An application to??environmental modelling, Environmental Modelling & Software, vol.34, pp.105-115, 2012.
DOI : 10.1016/j.envsoft.2011.06.006

URL : https://hal.archives-ouvertes.fr/halsde-00683551

]. T. Bed98 and . Bedford, Sensitivity indices for (tree-)dependent variables, SAMO98 Second International Symposium on Sensitivity Analysis of Model Output, pp.17-20, 1998.

]. D. Ber09 and . Bertsimas, Optimization methods, 2009.

]. G. Bla09 and . Blatman, Adaptive sparse polynomial chaos expansions for uncertainty propagation and sensitivity analysis, 2009.

]. E. Bibliography-[-bor07 and . Borgonovo, A new uncertainty importance measure, Reliability Engineering & System Safety, vol.92, pp.771-784, 2007.

]. J. Bou04 and . Boutahar, Méthodes de réduction et de propagation d'incertitudes: application à un modèle de chimie-transport pour la modélisation et la simulation des impacts, Bre95] L. Breiman. Better subset regression using the nonnegative garrote, pp.373-384, 1995.

S. [. Borgonovo and . Tarantola, Moment independent and variance-based sensitivity analysis with correlations: An application to the stability of a chemical reactor, International Journal of Chemical Kinetics, vol.108, issue.1-2, pp.687-698, 2008.
DOI : 10.1002/kin.20368

]. P. Buh06 and . Buhlmann, Boosting for high-dimensional linear models. The Annals of Statistics, pp.559-583, 2006.

]. P. Bvdg11, S. Buhlmann, . Van-de-geer-[-by10-]-p, B. Bühlmann, and . Yu, Statistics for highdimensional data, Boosting. Wiley Interdisciplinary Reviews: Computational Statistics, vol.2, issue.1, pp.69-74, 2010.

]. Y. Can12 and . Caniou, Analyse de sensibilité globale pour les modèles imbriqués et multiéchelles, 2012.

Y. [. Couvreur and . Bresler, On the Optimality of the Backward Greedy Algorithm for the Subset Selection Problem, SIAM Journal on Matrix Analysis and Applications, vol.21, issue.3, pp.797-808, 2000.
DOI : 10.1137/S0895479898332928

C. [. Champion, S. Cierco-ayrolles, M. Gadat, and . Vignes, Sparse regression and support recovery with l2-boosting algorithm, 2013.

F. [. Chastaing, C. Gamboa, . [. Prieur, F. Chastaing, C. Gamboa et al., Generalized Hoeffding-Sobol decomposition for dependent variables - application to sensitivity analysis, Electronic Journal of Statistics, vol.6, issue.0, pp.2420-2448, 2012.
DOI : 10.1214/12-EJS749

URL : https://hal.archives-ouvertes.fr/hal-00649404

S. [. Chun, N. I. Han, and . Tak, An uncertainty importance measure using a distance metric for the change in a cumulative distribution function, Reliability Engineering & System Safety, vol.70, issue.3, pp.313-321, 2000.
DOI : 10.1016/S0951-8320(00)00068-5

]. P. Cia98 and . Ciarlet, Introduction à l'analyse numérique matricielle et à l'optimisation. Dunod, 1998.

M. [. Cacuci, I. M. Ionescu-bujor, and . Navon, Sensitivity and Uncertainty Analysis, Volume II: Applications to Large- Scale Systems, 2005.
DOI : 10.1201/9780203483572

O. [. Crestaux, J. M. Maître, and . Martinez, Polynomial chaos expansion for sensitivity analysis, Reliability Engineering & System Safety, vol.94, issue.7, pp.1161-1172, 2009.
DOI : 10.1016/j.ress.2008.10.008

H. [. Cukier, K. E. 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

W. [. Cameron and . Martin, The Orthogonal Development of Non-Linear Functionals in Series of Fourier-Hermite Functionals, The Annals of Mathematics, vol.48, issue.2, pp.385-392, 1947.
DOI : 10.2307/1969178

]. R. Coo97 and . Cooke, Markov and entropy properties of tree-and vinedependent variables, Proceedings of the ASA Section of Bayesian Statistical Science, 1997.

R. [. Cukier, K. E. Schaibly, and . Shuler, Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. III. Analysis of the approximations, The Journal of Chemical Physics, vol.63, issue.3, pp.431-440, 1975.
DOI : 10.1063/1.431440

J. [. Charpentier and . Segers, Lower tail dependence for Archimedean copulas: Characterizations and pitfalls, Insurance: Mathematics and Economics, vol.40, issue.3, pp.525-532, 2007.
DOI : 10.1016/j.insmatheco.2006.08.004

N. [. Castle and . Shephard, The Methodology and Practice of Econometrics: A Festschrift in Honour of David F. Hendry, 2009.
DOI : 10.1093/acprof:oso/9780199237197.001.0001

B. [. Caniou and . Sudret, Distribution-based global sensitivity analysis using polynomial chaos expansions, Procedia social and behavioral sciences 2 Sixth International Conference on Sensitivity Analysis of Model Output, pp.7625-7626, 2009.
DOI : 10.1016/j.sbspro.2010.05.149

URL : http://doi.org/10.1016/j.sbspro.2010.05.149

N. [. Doucet, N. Freitas, and . Gordon, Sequential Monte Carlo methods in practice, Dev86] L. Devroye. Non-uniform random variate generation, 1986.
DOI : 10.1007/978-1-4757-3437-9

]. R. Dev98 and . Devore, Nonlinear approximation, Acta Numerica, pp.51-150, 1998.

D. [. Durrande, O. Ginsbourger, L. Roustant, and . Carraro, ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis, Journal of Multivariate Analysis, vol.115, issue.101, pp.57-67112, 1973.
DOI : 10.1016/j.jmva.2012.08.016

URL : https://hal.archives-ouvertes.fr/hal-00601472

F. [. Dick and . Pillichshammer, Digital nets and sequences: discrepancy theory and quasi-Monte Carlo integration, 2010.
DOI : 10.1017/CBO9780511761188

[. Rocquigny, La maîtrise des incertitues dans un contexte industriel-1ere partie: une approche méthodologique globale basée sue des exemples, Journal de la Société Française de Statistique, vol.147, issue.2, pp.33-71, 2006.

G. [. Droesbeke and . Saporta, Approches non paramétriques en régression, 2006.

[. Veiga, Analyse d'incertitudes et de sensibilité-Application aux modèles de cinétique chimique, 2007.

[. Veiga, F. Wahl, and F. Gamboa, Local Polynomial Estimation for Sensitivity Analysis on Models With Correlated Inputs, Technometrics, vol.51, issue.4, pp.452-463, 2009.
DOI : 10.1198/TECH.2009.08124

URL : https://hal.archives-ouvertes.fr/hal-00266102

T. [. Efron, I. Hastie, R. Johnstone, . [. Tibshirani, A. Embrechts et al., Least angle regression Correlation and dependence in risk management: properties and pitfalls. Risk management: value at risk and beyond The jackknife estimate of variance, The Annals of Statistics The Annals of Statistics, vol.32, issue.93, pp.407-451, 1981.

J. [. Frank and . Friedman, A Statistical View of Some Chemometrics Regression Tools, Technometrics, vol.5, issue.2, pp.109-148, 1993.
DOI : 10.1080/00401706.1993.10485033

[. Fang, K. Fang, and S. Kotz, The Meta-elliptical Distributions with Given Marginals, Journal of Multivariate Analysis, vol.82, issue.1, pp.1-16, 2002.
DOI : 10.1006/jmva.2001.2017

I. [. Fan and . Gijbels, Local polynomial modelling and its application, 1996.
DOI : 10.1007/978-1-4899-3150-4

]. R. Fis25 and . Fisher, Statistical methods for Research Workers, 1925.

[. Fang, S. Kotz, and K. Ng, Symmetric Multivariate and Related Distributions -Monographs on Statistics and Applied Probability, 1990.

P. [. Fisher and . Switzer, Graphical assessment of dependence. The American Statistician, pp.233-239, 2001.

]. F. Gal88 and . Galton, Co-relations and their measurement, chiefly from anthropometric data Detecting dependence with kendall plots, Proceedings of the Royal Society of London, pp.135-145275, 1888.

.. [. Genest and . Favre, Everything You Always Wanted to Know about Copula Modeling but Were Afraid to Ask, Journal of Hydrologic Engineering, vol.12, issue.4, pp.347-368, 2007.
DOI : 10.1061/(ASCE)1084-0699(2007)12:4(347)

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.8266

]. F. Gjk-+-13, A. Gamboa, T. Janon, A. Klein, C. Lagnoux-renaudie et al., Statistical inference for sobol pick freeze monte carlo method, 2013.

B. [. Genest and . Rémillard, Test of independence and randomness based on the empirical copula process, Test, vol.28, issue.2, pp.335-369, 2004.
DOI : 10.1007/BF02595777

P. [. Ghanem and . Spanos, Stochastic finite elements: a spectral approach, 2003.
DOI : 10.1007/978-1-4612-3094-6

]. C. Gu02 and . Gu, Smoothing Spline ANOVA Models, 2002.

]. G. Gvl96, C. F. Golub, and . Van-loan, Matrix computations, 1996.

R. [. Hora and . Iman, A comparison of maximum/bounding and bayesian/monte carlo for fault tree uncertainty analysis, 1986.

]. W. Hoe48 and . Hoeffding, A class of statistics with asymptotically normal distribution. The annals of, Mathematical Statistics, vol.19, issue.3, pp.293-325, 1948.

]. R. Hol88 and . Holmes, On random correlation matrices, 1988.

]. G. Hoo07, . Hookerhs96-]-t, A. Homma, and . Saltelli, Generalized functional anova diagnostics for highdimensional functions of dependent variables Importance measures in global sensitivity analysis of nonlinear models, Journal of Computational and Graphical Statistics Reliability Engineering & System Safety, vol.16, issue.521, pp.709-7321, 1996.

R. [. Hastie and . Tibshirani, Generalized additive models, Defense Technical Information Center, 1984.

R. [. Hastie, J. Tibshirani, and . Friedman, The elements of statistical learning Projection estimation in multiple regression with application to functional anova models. The annals of statistics, pp.242-272, 1998.

W. [. Iman and . Conover, A distribution-free approach to inducing rank correlation among input variables, Communications in Statistics - Simulation and Computation, vol.13, issue.4, pp.311-334, 1982.
DOI : 10.1080/00401706.1962.10490011

J. [. Iman, D. K. Davenport, and . Zeigler, Latin hypercube sampling (program user's guide), 1980.
DOI : 10.1002/0471667196.ess1084

S. [. Iman and . Hora, A Robust Measure of Uncertainty Importance for Use in Fault Tree System Analysis, Risk Analysis, vol.33, issue.3, pp.401-406, 1990.
DOI : 10.1111/j.1539-6924.1990.tb00523.x

]. B. Ioo11 and . Iooss, Revue sur l'analyse de sensibilité globale de modèles numériques, pp.1-23, 2011.

]. A. Jan12 and . Janon, Analyse de sensibilité et réduction de dimension-Application à l'océanographie, 2012.

]. J. Jen06 and . Jensen, Sur les fonctions convexes et les inégalités entre les valeurs moyennes, Acta Mathematica, vol.10, issue.1, pp.175-193, 1906.

T. A. Janon, A. Klein, M. Lagnoux-renaudie, C. Nodet, and . Prieur, Asymptotic normality and efficiency of two Sobol index estimators, ESAIM: Probability and Statistics, vol.18
DOI : 10.1051/ps/2013040

URL : https://hal.archives-ouvertes.fr/hal-00665048

J. Jacques, C. Lavergne, and N. Devictor, Sensitivity analysis in presence of model uncertainty and correlated inputs, Reliability Engineering & System Safety, vol.91, issue.10-11, pp.1126-1134, 2006.
DOI : 10.1016/j.ress.2005.11.047

URL : https://hal.archives-ouvertes.fr/hal-00194061

M. [. Janon, C. Nodet, and . 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

]. C. Kel87 and . Kelley, Iterative methods for optimization, 1987.

]. M. Ken38 and . Kendall, A new measure of rank correlation, Biometrika, vol.30, pp.81-93, 1938.

H. [. Kuipers and . Niederreiter, Uniform distribution of sequences, 2006.

. [. Kharoubi-rakotomalala, Les fonctions copules en finance, 2008.

]. R. Kre98 and . Kress, Numerical analysis, 1998.

]. W. Kru58, Ordinal measures of association, Journal of the American Statistical Association, vol.53, issue.284, pp.814-861, 1958.

S. [. Kucherenko, P. Tarantola, and . Annoni, Estimation of global sensitivity indices for models with dependent variables, Computer Physics Communications, vol.183, issue.4, pp.937-946, 2012.
DOI : 10.1016/j.cpc.2011.12.020

G. [. Kimeldorf and . Wahba, Spline functions and stochastic processes, Sankhya, vol.32, issue.2, pp.173-180, 1970.

P. [. Lee, R. C. Bartlett, and . Williamson, On efficient agnostic learning of linear combinations of basis functions, Proceedings of the eighth annual conference on Computational learning theory , COLT '95, pp.369-376, 1995.
DOI : 10.1145/225298.225343

X. [. Liu and . Chen, Nonparametric greedy algorithms for the sparse learning problem, proceedings in advances in neural information processing systems, 2009.

R. [. Lewandowski, R. J. Cooke, and . Tebbens, Sample-based estimation of correlation ratio with polynomial approximation, ACM Transactions on Modeling and Computer Simulation, vol.18, issue.1, pp.1-17, 2007.
DOI : 10.1145/1315575.1315578

A. [. Lebrun and . Dutfoy, A generalization of the Nataf transformation to distributions with elliptical copula, Probabilistic Engineering Mechanics, vol.24, issue.2, pp.172-178, 2009.
DOI : 10.1016/j.probengmech.2008.05.001

]. R. Leb13 and . Lebrun, Contributions à la modélisation de la dépendance stochastique, 2013.

]. E. Leh66 and . Lehmann, Some concepts of dependence, The Annals of Mathematical Statistics, vol.35, issue.5, pp.1139-1153, 1966.

]. C. Lem09 and . Lemieux, Monte Carlo and Quasi-Monte Carlo sampling. Series in Statistics, 2009.

D. [. Lamboni, H. Makowski, and . Monod, Indices de sensibilité , sélection de paramètres et erreur quadratique de prédiction: des liaisons dangereuses ?, pp.26-48, 2011.

]. G. Lr10a, H. Li, and . Rabitz, D-morph regression: application to modeling with unknown parameters more than observation datamorph regression:application to modeling with unknown parameters more than observation data, Journal of mathematical chemistry Journal of Mathematical Chemistry, vol.48, issue.48, pp.1010-10351010, 2010.

H. [. Li and . Rabitz, General formulation of HDMR component functions with independent and correlated variables, Journal of Mathematical Chemistry, vol.73, issue.4, pp.99-130, 2012.
DOI : 10.1007/s10910-011-9898-0

]. G. Lrh-+-08, H. Li, J. Rabitz, Z. Hu, Y. Chen et al., Regularized random-sampling high dimensional model representation, Journal of Mathematical Chemistry, vol.43, issue.3, pp.6022-6032, 2008.

C. [. Li, H. Rosenthal, and . Rabitz, High Dimensional Model Representations, The Journal of Physical Chemistry A, vol.105, issue.33, pp.7765-7777, 2001.
DOI : 10.1021/jp010450t

]. G. Lry-+-10, H. Li, P. E. Rabitz, O. Yelvington, F. Oluwole et al., Global sensitivity analysis with independent and/or correlated inputs, Journal of Physical Chemistry A, vol.114, pp.6022-6032, 2010.

]. D. Lue97 and . Luenberger, Optimization by vector space methods, 1997.

H. [. Lin and . Zhang, Component selection and smoothing in multivariate nonparametric regression. The Annals of Statistics, pp.2272-2297, 2006.

]. M. Mck97 and . Mckay, Nonparametric variance-based methods of assessing uncertainty importance, Reliability Engineering & System Safety, vol.57, issue.3, pp.267-279, 1997.

]. C. Mey00 and . Meyer, Matrix analysis and applied linear algebra, SIAM, 2000.

]. C. Mey12 and . Meynet, Sélection de variables pour la classification non supervisée en grande dimension, 2012.

B. [. Marrel, B. Iooss, O. Laurent, and . 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

C. [. Monod, D. Naud, and . Makowski, Uncertainty and sensitivity analysis for crop models In Working with Dynamic Crop Models: Evaluation, Analysis, Parameterization and Applications, pp.55-100, 2006.

]. D. Mor56 and . Morgenstern, Einfache beispiele zweidimensionaler verteilungen, Mitteilingsblatt fur Mathematische Statistik, pp.234-253, 1956.

F. [. Marchi, F. Rojas, and . Louzada, The chi-plot and its asymptotic confidence interval for analyzing bivariate dependence: An application to the average intelligence and atheism rates across nations data, Journal of Data Science, vol.10, pp.711-722, 2012.

[. Mara and S. Tarantola, Variance-based sensitivity indices for models with dependent inputs, Reliability Engineering & System Safety, vol.107, pp.115-121, 2012.
DOI : 10.1016/j.ress.2011.08.008

URL : https://hal.archives-ouvertes.fr/hal-01093038

Z. [. Mallat and . Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, vol.41, issue.12, pp.3397-3415, 1993.
DOI : 10.1109/78.258082

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.335.5769

]. A. Nat62 and . Nataf, Détermination des distributions de probabilité dont les marges sont données, Compte rendus de l'Académie des Sciences, 1962.

]. J. Nel65 and . Nelder, The analysis of randomized experiments with orthogonal block structure. i. block structure and the null analysis of variance, Proceedings of the Royal Society of London, pp.147-162, 1965.

]. R. Nel06 and . Nelsen, An introduction to copulas, 2006.

]. D. Oak82 and . Oakes, A model for association in bivariate survival data, Journal of the Royal Statistical Society. Series B (Methodological ), vol.44, issue.3, pp.414-422, 1982.

A. [. Oakley and . Hagan, Probabilistic sensitivity analysis of complex models: a Bayesian approach, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.34, issue.3, pp.751-769, 2004.
DOI : 10.1214/ss/1009213004

B. [. Osborne, B. A. Presnell, and . Turlach, On the lasso and its dual, Journal of Computational and Graphical Statistics, vol.9, issue.2, pp.319-337, 2000.

]. M. Osb00 and . Osborne, A new approach to variable selection in least squares problems, Journal of numerical analysis, vol.20, pp.389-404, 2000.

]. A. Owe94 and . Owen, Lattice sampling revisited:monte carlo variance of means over randomized orthogonal arrays. The Annals of Statistics, pp.930-945, 1994.

]. A. Owe05 and . Owen, Multidimensional variation for quasi-Monte Carlo, International Conference on Statistics in honour of Professor Kai-Tai Fang's 65th birthday, pp.49-74, 2005.

[. Parzen, On estimation of a probability density function and mode. The annals of mathematical statistics BIBLIOGRAPHY [Pea96] K. Pearson. Mathematical contributions to the theory of evolution . On a form of spurious correlation which may arise when indices are used in the measurement of organs, In Proceedings of the Royal Society of London, vol.33, issue.60, pp.1065-1076, 1896.

]. Pou11 and . Pougaza, Utilisation de la notion de copule en tomographie, 2011.

C. [. Rasmussen and . Williams, Gaussian Processes in Machine Learning, 2006.
DOI : 10.1162/089976602317250933

]. R. Sch90 and . Schapire, The strength of weak learnability, Machine learning, vol.5, issue.2, pp.197-227, 1990.

R. [. Soize and . Ghanem, Physical Systems with Random Uncertainties: Chaos Representations with Arbitrary Probability Measure, SIAM Journal on Scientific Computing, vol.26, issue.2, pp.395-410, 2004.
DOI : 10.1137/S1064827503424505

URL : https://hal.archives-ouvertes.fr/hal-00686211

]. A. Skl71 and . Sklar, Random variables, joint distribution functions, and copulas, Kybernetika, vol.9, issue.3, pp.449-460, 1971.

W. [. Sherman and . Morrison, Adjustment of an inverse matrix corresponding to a change in one element of a given matrix. The Annals of Mathematical Statistics [Sob67] I.M. Sobol. On the distribution of points in a cube and the approximate evaluation of integrals, Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki, pp.124-127784, 1950.

]. I. Sob76 and . Sobol, Uniformly distributed sequences with an addition uniform property, USSR Computational Mathematics and Mathematical Physics, vol.16, issue.5, pp.236-242, 1976.

]. I. Sob93 and . Sobol, Sensitivity estimates for nonlinear mathematical models, Mathematical Modeling and Computational Experiment, vol.1, issue.4, pp.407-414, 1993.

]. I. Sob01 and . Sobol, Global sensitivity indices for nonlinear mathematical models and their monte carlo estimates, Mathematics and Computers in Simulations, vol.55, pp.271-280, 2001.

]. A. Sra-+-08, M. Saltelli, T. Ratto, F. Andres, J. Campolongo et al., Global sensitivity analysis: The primer, 2008.

A. Saltelli and S. Tarantola, On the Relative Importance of Input Factors in Mathematical Models, Journal of the American Statistical Association, vol.97, issue.459, pp.2811-2828, 2002.
DOI : 10.1198/016214502388618447

S. [. Saltelli, K. S. Tarantola, and . Chan, A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output, Technometrics, vol.60, issue.1, pp.39-56, 1999.
DOI : 10.1007/BF01166355

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

]. C. Sto94 and . Stone, The use of polynomial splines and their tensor products in multivariate function estimation. The Annals of Statistics, pp.118-171, 1994.

]. B. Sud08 and . Sudret, Global sensitivity analysis using polynomial chaos expansion. Reliability engineering and system safety, pp.964-979, 2008.

W. [. Sacks, T. J. Welch, H. P. Mitchell, and . Wynn, Design and Analysis of Computer Experiments, Statistical Science, vol.4, issue.4, pp.409-423, 1989.
DOI : 10.1214/ss/1177012413

B. [. Santner, W. I. Williams, and . Notz, The design and analysis of computer experiments, 2003.
DOI : 10.1007/978-1-4757-3799-8

]. V. Tem11 and . Temlyakov, Greedy approximation. Cambridge monographs on applied and computational mathematics, 2011.

D. [. Tarantola, T. Gatelli, ]. J. Maratib96, and . Tibshirani, Random balance designs for the estimation of first order global sensitivity indices. Reliability Engineering & System Safety Regression shrinkage and selection via the lasso The Lasso problem and uniqueness, Journal of the Royal Statistical Society, vol.91, issue.581, pp.717-727267, 1996.
URL : https://hal.archives-ouvertes.fr/hal-01065897

]. J. Tis12 and . Tissot, Sur la décomposition ANOVA et l'estimation des indices de Sobol'. Application à un modèle d'écosystème marin, 2012.

]. S. Tou11 and . Touzani, Méthodes de surface de réponse basées sur la décomposition de la variance fonctionnelle et application à l'analyse de sensibilité Tissot and C. Prieur. A bias correction method for the estimation of sensitivity indices based on random balance designs, Reliability Engineering & System Safety, pp.205-213, 2011.

]. J. Tp12b, C. Tissot, and . Prieur, Variance-based sensitivity analysis using harmonic analysis Disponible à http Copula modeling: an introduction for practitioners, Foundations and Trends in Econometrics, vol.1, pp.1-111, 2005.

. W. Vdv98-]-a, . Van-der, and . Vaart, Asymptotic Statistics, 1998.

]. B. Vdw52 and . Van-der-waerden, Order tests for the two-sample problem and their power, vM13] R. von Mises. Mechanik der festen körper im plastisch deformablen zustand, pp.458582-592, 1913.

]. S. Wei80 and . Weisberg, Applied linear regression [Wey16] Hermann Weyl. Über die gleichverteilung von zahlen mod. eins, Mathematische Annalen, vol.77, issue.3, pp.313-352, 1916.

P. [. Weiss, M. Holmes, and . Hardy, A course in probability, 2006.

]. N. Wie38 and . Wiener, The homogeneous chaos, American Journal of Mathematics, vol.60, issue.4, pp.897-936, 1938.

G. [. Xu and . Gertner, Extending a global sensitivity analysis technique to models with correlated parameters, Computational Statistics & Data Analysis, vol.51, issue.12, pp.5579-5590, 2007.
DOI : 10.1016/j.csda.2007.04.003

G. [. Xu and . Gertner, Uncertainty and sensitivity analysis for models with correlated parameters, Reliability Engineering & System Safety, vol.93, issue.10, pp.1563-1573, 2008.
DOI : 10.1016/j.ress.2007.06.003

]. T. Zha11 and . Zhang, Adaptive forward-backward algorithm for learning sparse representations, IEEE transactions on information theory, vol.57, issue.7, pp.4689-4708, 2011.

S. [. Zuniga, N. Kucherenko, and . Shah, Metamodelling with independent and dependent inputs, Computer Physics Communications, vol.184, issue.6, pp.1570-1580, 2013.
DOI : 10.1016/j.cpc.2013.02.005