A. N. Avramidis and J. R. Wilson, Correlation-Induction Techniques for Estimating Quantiles in Simulation Experiments, Operations Research, vol.46, issue.4, pp.574-591, 1988.
DOI : 10.1287/opre.46.4.574

H. A. David, Order statistics, 1981.

R. Davidson and J. G. Mackinnon, Regression-based methods for using control variates in Monte Carlo experiments, Journal of Econometrics, vol.54, issue.1-3, pp.203-222, 1992.
DOI : 10.1016/0304-4076(92)90106-2

T. Dielman, C. Lowry, and R. Pfaffenberger, A comparison of quantile estimators, Communications in Statistics - Simulation and Computation, vol.12, issue.2, pp.355-371, 1994.
DOI : 10.1002/9780470316481

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

G. S. Fishman, Monte Carlo concepts, algorithms, and applications, 1996.

P. Glasserman, P. Heidelberger, and P. Shahabuddin, Stratification issues in estimating value-at-risk, Proceedings of the 31st conference on Winter simulation Simulation---a bridge to the future, WSC '99, pp.351-359, 1998.
DOI : 10.1145/324138.324241

P. Glynn, Importance sampling for Monte Carlo estimation of quantiles, Mathematical Methods in Stochastic Simulation and Experimental Design: Proceedings of the 2nd St. Petersburg Workshop on Simulation, pp.180-185, 1996.

T. J. Hastie and R. J. Tibshirani, Generalized additive models, 1990.

T. C. Hesterberg, Control variates and importance sampling for the bootstrap, Proceedings of the Statistical Computing Section of the American Statistical Association, pp.40-48, 1993.

T. C. Hesterberg, Weighted Average Importance Sampling and Defensive Mixture Distributions, Technometrics, vol.29, issue.5, pp.185-194, 1995.
DOI : 10.1080/00401706.1995.10484303

T. C. Hesterberg and B. L. Nelson, Control Variates for Probability and Quantile Estimation, Management Science, vol.44, issue.9, pp.1295-1312, 1998.
DOI : 10.1287/mnsc.44.9.1295

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.329.7345

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

J. C. Hsu and B. L. Nelson, Control variates for quantile estimation, Proceedings of the 1987 Winter Simulation Conference, pp.434-444, 1987.

D. Jones, M. Schonlau, W. , and W. , Efficient global optimization of expensive black-box functions, Journal of Global Optimization, vol.13, issue.4, pp.455-492, 1998.
DOI : 10.1023/A:1008306431147

A. M. Law and W. D. Kelton, Simulation modeling and analysis, 1991.

D. K. Lin, A New Class of Supersaturated Designs, Technometrics, vol.100, issue.1, pp.28-31, 1993.
DOI : 10.2307/1266468

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, submitted. Available at URL: http://fr.arxiv.org/abs/0802, 1008v1 Nelson, B. L. Oper. Res, vol.38, pp.974-992, 1990.
URL : https://hal.archives-ouvertes.fr/hal-00239492

W. T. Nutt and G. B. Wallis, Evaluation of nuclear safety from the outputs of computer codes in the presence of uncertainties, Reliability Engineering & System Safety, vol.83, issue.1, pp.57-77, 2004.
DOI : 10.1016/j.ress.2003.08.008

J. Oakley, Estimating percentiles of uncertain computer code outputs, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.14, issue.1, pp.83-93, 2004.
DOI : 10.1111/1467-9884.00300

M. S. Oh and J. O. Berger, Adaptive importance sampling in monte carlo integration, Journal of Statistical Computation and Simulation, vol.2, issue.3-4, pp.41-143, 1992.
DOI : 10.1016/0304-4076(85)90030-2

P. Ranjan, D. Bingham, and G. Michailidis, Sequential Experiment Design for Contour Estimation From Complex Computer Codes, Technometrics, vol.50, issue.4, 2008.
DOI : 10.1198/004017008000000541

C. R. Rao, Linear statistical inference and its applications Simulation and the Monte Carlo method, 1973.

B. Rutherford, A response-modeling alternative to surrogate models for support in computational analyses, Reliability Engineering & System Safety, vol.91, issue.10-11, pp.1322-1330, 2006.
DOI : 10.1016/j.ress.2005.11.050

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

M. Schonlau and W. J. Welch, Screening the input variables to a computer model via analysis of variance and visualization. In Screening methods for experimentation and industry, drug discovery and genetics, pp.308-327, 2005.

E. Vazquez, P. Martinez, and M. , Estimation of the volume of an excursion set of a Gaussian process using intrinsic kriging Available at URL: http://arxiv.org/abs/math, Journal of Statistical Planning and Inference, p.611273, 2007.

E. Volkova, B. Iooss, V. Dorpe, and F. , Global sensitivity analysis for a numerical model of radionuclide migration, from the RRC " Kurchatov Institute " radwaste disposal site, Stochastic Environmental Research and Risk Assesment, pp.17-31, 2008.