C. Cannamela, J. Garnier, and B. Iooss, Controlled stratification for quantile estimation, The Annals of Applied Statistics, vol.2, issue.4, pp.1554-1580, 2008.
DOI : 10.1214/08-AOAS186
URL : https://hal.archives-ouvertes.fr/hal-00355016

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.238, issue.12, pp.3561-3578, 2008.
DOI : 10.1016/j.nucengdes.2008.06.004

K. Fang, Wrap-Around L2-Discrepancy of Random Sampling, Latin Hypercube and Uniform Designs, Journal of Complexity, vol.17, issue.4, pp.608-624, 2001.
DOI : 10.1006/jcom.2001.0589

K. Fang, R. Li, and A. Sudjianto, Design and modeling for computer experiments, 2006.
DOI : 10.1201/9781420034899

V. Feuillard, Analyse d'une base de données pour la calibration d'un code de calcul, Thèse de l, 2007.

J. Franco, Planification dexpériences numériques en phase exploratoire pour la simulation des phénomènes complexes, Thèse de l'Ecole Nationale Supérieure des Mines de Saint-Etienne, 2007.

B. Iooss, Numerical Study of the Metamodel Validation Process, 2009 First International Conference on Advances in System Simulation, pp.100-105, 2009.
DOI : 10.1109/SIMUL.2009.8

B. Iooss, L. Boussouf, A. Marrel, and V. Feuillard, Numerical study of algorithms for metamodel construction and validation, Safety, reliability and risk analysis -Proceedings of the ESREL 2008 Conference, pp.2135-2141, 2008.

B. Iooss, F. Van-dorpe, and N. Devictor, Response surfaces and sensitivity analyses for an environmental model of dose calculations, Reliability Engineering & System Safety, vol.91, issue.10-11, pp.1241-1251, 2006.
DOI : 10.1016/j.ress.2005.11.021

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, issue.1, pp.268-287, 2005.
DOI : 10.1016/j.jspi.2004.02.014

M. E. Johnson, L. M. Moore, and D. Ylvisaker, Minimax and maximin distance designs, Journal of Statistical Planning and Inference, vol.26, issue.2, pp.131-148, 1990.
DOI : 10.1016/0378-3758(90)90122-B

B. Jones and R. T. Johnson, Design and analysis for the Gaussian process model, Quality and Reliability Engineering International, vol.138, issue.4, pp.515-524, 2009.
DOI : 10.1002/qre.1044

J. P. Kleijnen and R. G. Sargent, A methodology for fitting and validating metamodels in simulation1Two anonymous referees' comments on the first draft lead to an improved organization of our paper.1, European Journal of Operational Research, vol.120, issue.1, pp.14-29, 2000.
DOI : 10.1016/S0377-2217(98)00392-0

C. Lemieux, Monte Carlo and quasi-Monte Carlo sampling, 2009.

M. Liefvendahl and R. Stocki, A study on algorithms for optimization of Latin hypercubes, Journal of Statistical Planning and Inference, vol.136, issue.9, pp.3231-3247, 2006.
DOI : 10.1016/j.jspi.2005.01.007

A. Marrel, B. Iooss, F. Van-dorpe, and E. Volkova, An efficient methodology for modeling complex computer codes with Gaussian processes, Computational Statistics & Data Analysis, vol.52, issue.10, pp.4731-4744, 2008.
DOI : 10.1016/j.csda.2008.03.026
URL : https://hal.archives-ouvertes.fr/hal-00239492

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

J. D. Martin and T. W. Simpson, Use of Kriging Models to Approximate Deterministic Computer Models, AIAA Journal, vol.43, issue.4, pp.853-863, 2005.
DOI : 10.2514/1.8650

M. D. Mckay, R. J. Beckman, and W. J. Conover, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, vol.21, pp.239-245, 1979.

M. Meckesheimer, A. J. Booker, R. R. Barton, and T. W. Simpson, Computationally Inexpensive Metamodel Assessment Strategies, AIAA Journal, vol.40, issue.10, pp.2053-2060, 2002.
DOI : 10.2514/2.1538

M. D. Morris and T. J. Mitchell, Exploratory designs for computational experiments, Journal of Statistical Planning and Inference, vol.43, issue.3, pp.381-402, 1995.
DOI : 10.1016/0378-3758(94)00035-T

J. Park, Optimal Latin-hypercube designs for computer experiments, Journal of Statistical Planning and Inference, vol.39, issue.1, pp.95-111, 1993.
DOI : 10.1016/0378-3758(94)90115-5

M. I. Reis, A. M. Santos, and . Nova, Statistical fitting and validation of non-linear simulation metamodels: A case study, European Journal of Operational Research, vol.171, issue.1, pp.53-63, 2006.
DOI : 10.1016/j.ejor.2004.07.067

G. Rennen, Subset selection from large datasets for kriging modeling. Structural and Multidisciplinary Optimization 38, pp.545-569, 2009.

J. Sacks, W. J. Welch, T. J. Mitchell, and H. P. Wynn, Design and Analysis of Computer Experiments, Statistical Science, vol.4, issue.4, pp.409-435, 1989.
DOI : 10.1214/ss/1177012413

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

T. Simpson, J. Peplinski, P. Kock, and J. Allen, Metamodels for Computer-based Engineering Design: Survey and recommendations, Engineering with Computers, vol.17, issue.2, pp.129-150, 2001.
DOI : 10.1007/PL00007198

M. Stein, Large Sample Properties of Simulations Using Latin Hypercube Sampling, Technometrics, vol.17, issue.2, pp.143-151, 1987.
DOI : 10.1080/00401706.1987.10488205