Aas K., Modelling the dependence structure of financial assets: a survey of four copulas, Norwegian Computing Center report nr. SAMBA/22/04, December 2004. pdf


Abate, J. and Whitt, W. (1992). The Fourier-series method for inverting transforms of probability distributions. Queueing Systems 10, 5–88., 1992, formula 5.5. pdf


Pierre-Olivier Amblard, Jean-François Coeurjolly, Frédéric Lavancier, Anne Philippe, Basic properties of the Multivariate Fractional Brownian Motion, pdf


Au, S. K. Estimation of small failure probabilities in high dimensions by subset simulation. Prob. Eng. Mech., 2001, 16(4), 263-277. pdf


Bhattacharyya G.K., and R.A. Johnson, Statistical Concepts and Methods, John Wiley and Sons, New York, 1997.


Blatman, G. Adaptive sparse polynomial chaos expansions for uncertainty propagation and sensitivity analysis., PhD thesis. Blaise Pascal University-Clermont II, France, 2009. pdf


Burnham, K.P., and Anderson, D.R. Model Selection and Multimodel Inference: A Practical Information Theoretic Approach, Springer, 2002.


Mathieu Cambou, Marius Hofert, Christiane Lemieux, Quasi-Random numbers for copula models, Stat. Comp., 2017, 27(5), 1307-1329. pdf


Caniou, Y. Global sensitivity analysis for nested and multiscale modelling. PhD thesis. Blaise Pascal University-Clermont II, France, 2012. pdf


D’Agostino, R.B. and Stephens, M.A. Goodness-of-Fit Techniques, Marcel Dekker, Inc., New York, 1986.


G. Damblin, M. Couplet and B. Iooss. Numerical studies of space filling designs: optimization of Latin hypercube samples and subprojection properties. Journal of Simulation, 7:276-289, 2013. pdf


Devroye L, Non-Uniform RandomVariate Generation, Springer-Verlag, New York, 1986 pdf


Dixon, W.J., Massey, F.J, Introduction to statistical analysis 4th ed., McGraw-Hill, 1983


Doornik, J.A. An Improved Ziggurat Method to Generate Normal Random Samples, mimeo, Nuffield College, University of Oxford, 2005. pdf


Dubourg, V. Adaptative surrogate models for reliability and reliability-based design optimization, University Blaise Pascal - Clermont II, 2011. pdf


K-T. Fang, R. Li, and A. Sudjianto. Design and modeling for computer experiments. Chapman & Hall CRC, 2006.


Gamboa, F., Janon, A., Klein, T. & Lagnoux, A. Sensitivity analysis for multidimensional and functional outputs. 2013 pdf


Hormann W., The generation of Binomial Random Variates pdf


Nathan Halko, Per-Gunnar Martinsson, Joel A. Tropp, Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions, pdf


Nathan Halko, Per-Gunnar Martisson, Yoel Shkolnisky and Mark Tygert, An algorithm for the principal component analysis of large data sets, pdf


Janon A., Klein T., Lagnoux-Renaudie A., Prieur C., Asymptotic normality and efficiency of two Sobol index estimators, ESAIM: Probability and Statistics, EDP Sciences, 2014, 18, pp.342-364. pdf


Jansen, M.J.W. Analysis of variance designs for model output, Computer Physics Communication, 1999, 117, 35-43. pdf


R. Jin, W. Chen, and A. Sudjianto. An efficient algorithm for constructing optimal design of computer experiments. Journal of Statistical Planning and Inference, 134 :268-287, 2005. pdf


Johnson M, Moore L and Ylvisaker D (1990). Minimax and maximin distance design. Journal of Statistical Planning and Inference 26(2): 131-148.


Donald R. Jones, Matthias Schonlau and William J Welch. Global optimization of expensive black-box functions, Journal of Global Optimization, 13(4), 455-492, 1998. pdf


Knight, W. R. A Computer Method for Calculating Kendall’s Tau with Ungrouped Data. Journal of the American Statistical Association, 1966, 61(314, Part 1), 436-439. pdf


Koay C.G., Basser P.J., Analytically exact correction scheme for signal extraction from noisy magnitude MR signals, Journal of magnetics Resonance 179, 317-322, 2006.


J.R. Koehler and A.B. Owen. Computer experiments. In S. Ghosh and C.R. Rao, editors, Design and analysis of experiments, volume 13 of Handbook of statistics. Elsevier, 1996.


Lebrun, R. & Dutfoy, A. An innovating analysis of the Nataf transformation from the copula viewpoint. Prob. Eng. Mech., 2009, 24, 312-320. pdf


Lebrun, R. & Dutfoy, A. A generalization of the Nataf transformation to distributions with elliptical copula. Prob. Eng. Mech., 2009, 24, 172-178. pdf


Lebrun, R. & Dutfoy, A. Do Rosenblatt and Nataf isoprobabilistic transformations really differ? Prob. Eng. Mech., 2009, 24, 577-584. pdf


L’Ecuyer P., Lemieux C. (2005) Recent Advances in Randomized Quasi-Monte Carlo Methods. In: Dror M., L’Ecuyer P., Szidarovszky F. (eds) Modeling Uncertainty. International Series in Operations Research & Management Science, vol 46. Springer, Boston, MA pdf


Marsaglia G. and Tsang W. W., A Simple Method for Generating Gamma, Journal of Statistical Computational and Simulation, vol 46, pp101 - 110,1993.


Martinez, J-M., Analyse de sensibilite globale par decomposition de la variance, Presentation in the meeting of GdR Ondes and GdR MASCOT-NUM, January, 13th, 2011, Institut Henri Poincare, Paris, France


G. Matthys & J. Beirlant, Estimating the extreme value index and high quantiles with exponential regression models, Statistica Sinica, 13, 850-880, 2003. pdf


J. A. Mauricio, Exact Maximum Likelihood Estimation of Stationary Vector ARMA Models, Journal of the American Statistical Association 90, 282-291, 1995. pdf


McKay M, Beckman R and Conover W (1979). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2): 239-245. pdf


J. C. Meza, R. A. Oliva, P. D. Hough, and P. J. Williams., OPT++: an object oriented toolkit for nonlinear optimization, ACM Transactions on Mathematical Software, 33(2), 2007. pdf


Thomas P. Minka, Estimating a Dirichlet distribution, Microsoft Research report, 2000 (revised 2003, 2009, 2012). pdf


D. Morris and J. Mitchell. Exploratory designs for computational experiments. Journal of Statistical Planning and Inference, 43 :381-402, 1995. pdf


M. Munoz Zuniga, J. Garnier, E. Remy and E. de Rocquigny, Adaptative Directional Stratification for controlled estimation of the probability of a rare event, Reliability Engineering and System Safety, 2011. pdf


Nataf, A. Determination des distributions dont les marges sont donnees. C. R. Acad. Sci. Paris, 1962, 225, 42-43. pdf


Stephen G. Nash, 1999, A survey of Truncated-Newton methods, Systems Engineering and Operations Research Dept., George Mason University, Fairfax, VA 22030. pdf


NIST/SEMATECH e-Handbook of Statistical Methods,


Steven G. Johnson, The NLopt nonlinear-optimization package,


Dumas A., Lois asymptotiques des estimateurs des indices de Sobol’, Technical report, Phimeca, 2018. pdf


Pronzato L and Muller W (2012). Design of computer experiments: Space filling and beyond. Statistics and Computing 22(3): 681-701. pdf


Rai, P. Sparse Low Rank Approximation of Multivariate Functions - Applications in Uncertainty Quantification., PhD thesis. Ecole Centrale de Nantes, France, 2015. pdf


Rosenblatt, M. Remarks on a multivariate transformation. Ann. Math. Stat., 1952, 23, 470-472. pdf


Saltelli, A., Tarantola, S. & Chan, K. A quantitative, model independent method for global sensitivity analysis of model output. Technometrics, 1999, 41(1), 39-56. pdf


Saltelli, A. Making best use of model evaluations to compute sensitivity indices. Computer Physics Communication, 2002, 145, 580-297. pdf


Saporta, G. (1990). Probabilités, Analyse de données et Statistique, Technip


Simard, R. & L’Ecuyer, P. Computing the Two-Sided Kolmogorov- Smirnov Distribution. Journal of Statistical Software, 2011, 39(11), 1-18. pdf


Sobol, I. M. Sensitivity analysis for non-linear mathematical model Math. Modelling Comput. Exp., 1993, 1, 407-414. pdf


Sobol, I.M., Tarantola, S., Gatelli, D., Kucherenko, S.S. and Mauntz, W. Estimating the approximation errors when fixing unessential factors in global sensitivity analysis, Reliability Engineering and System Safety, 2007, 92, 957-960. pdf


Soize, C., Ghanem, R. Physical systems with random uncertainties: Chaos representations with arbitrary probability measure, SIAM Journal on Scientific Computing, Society for Industrial and Applied Mathematics, 2004, 26 (2), 395-410. pdf


Sprent, P., and Smeeton, N.C. Applied Nonparametric Statistical Methods, Third edition, Chapman & Hall, 2001.


Stadlober E., The ratio of uniforms approach for generating discrete random variates. Journal of Computational and Applied Mathematics, vol. 31, no. 1, pp. 181-189, 1990. pdf


Stoer, J., Bulirsch, R. Introduction to Numerical Analysis, Second Edition, Springer-Verlag, 1993. pdf


Wand M.P, Jones M.C. Kernel Smoothing First Edition, Chapman & Hall, 1994.