S. Asmussen and P. W. Glynn, Stochastic Simulation, 2007.

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, 1998.
DOI : 10.1287/opre.46.4.574

T. Avramidis and P. L-'ecuyer, Efficient Monte Carlo and Quasi???Monte Carlo Option Pricing Under the Variance Gamma Model, Management Science, vol.52, issue.12, pp.1930-1944, 2006.
DOI : 10.1287/mnsc.1060.0575

T. Avramidis, P. L-'ecuyer, and P. A. Tremblay, Efficient simulation of gamma and variance-gamma processes, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693), pp.319-326, 2003.
DOI : 10.1109/WSC.2003.1261439

URL : https://eprints.soton.ac.uk/55793/1/wsc03vg.pdf

R. E. Caflisch, W. Morokoff, and A. Owen, Valuation of mortgage-backed securities using Brownian bridges to reduce effective dimension, The Journal of Computational Finance, vol.1, issue.1, pp.27-46, 1997.
DOI : 10.21314/JCF.1997.005

S. Chen, J. Dick, and A. B. Owen, Consistency of Markov chain quasi-Monte Carlo on continuous state spaces, The Annals of Statistics, vol.39, issue.2, pp.673-701, 2011.
DOI : 10.1214/10-AOS831

R. Cranley and T. N. Patterson, Randomization of Number Theoretic Methods for Multiple Integration, SIAM Journal on Numerical Analysis, vol.13, issue.6, pp.904-914, 1976.
DOI : 10.1137/0713071

V. Demers, P. L-'ecuyer, and B. Tuffin, A combination of randomized quasi-Monte Carlo with splitting for rare-event simulation, Proceedings of the 2005 European Simulation and Modeling Conference, pp.25-32, 2005.

J. Dick and F. Pillichshammer, Digital Nets and Sequences: Discrepancy Theory and Quasi-Monte Carlo Integration, 2010.
DOI : 10.1017/CBO9780511761188

J. Dick, I. H. Sloan, X. Wang, and H. Wo´zniakowskiwo´zniakowski, Good Lattice Rules in Weighted Korobov Spaces with General Weights, Numerische Mathematik, vol.103, issue.1, pp.63-97, 2006.
DOI : 10.1007/s00211-005-0674-6

M. Dion and P. L-'ecuyer, American option pricing with randomized quasi-Monte Carlo simulations, Proceedings of the 2010 Winter Simulation Conference, pp.2705-2720, 2010.
DOI : 10.1109/WSC.2010.5678966

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.172.6870

C. Doerr, M. Gnewuch, and M. Wahlström, Calculation of Discrepancy Measures and Applications, pp.621-678, 2014.
DOI : 10.1007/978-3-319-04696-9_10

URL : https://hal.archives-ouvertes.fr/hal-01086555

E. Haddad, R. Lécot, C. L-'ecuyer, P. Nassif, and N. , Quasi-Monte Carlo methods for Markov chains with continuous multi-dimensional state space, Mathematics and Computers in Simulation, vol.81, issue.3, pp.560-567, 2010.
DOI : 10.1016/j.matcom.2010.07.027

URL : https://hal.archives-ouvertes.fr/hal-00949602

H. Faure, Discrépance des suites associéesassociées`associéesà un système de numération en dimension s, Acta Arithmetica, vol.61, pp.337-351, 1982.

M. Gerber and N. Chopin, Sequential quasi Monte Carlo, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.59, issue.3, pp.509-579, 2015.
DOI : 10.1287/opre.1100.0853

URL : http://arxiv.org/abs/1402.4039

S. Haber, A modified Monte-Carlo quadrature, Mathematics of Computation, vol.20, issue.95, pp.361-368, 1966.
DOI : 10.1090/S0025-5718-1966-0210285-0

F. J. Hickernell, The mean square discrepancy of randomized nets, ACM Transactions on Modeling and Computer Simulation, vol.6, issue.4, pp.274-296, 1996.
DOI : 10.1145/240896.240909

F. J. Hickernell, A generalized discrepancy and quadrature error bound, Mathematics of Computation of the American Mathematical Society, vol.67, issue.221, pp.299-322, 1998.
DOI : 10.1090/S0025-5718-98-00894-1

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.100.2225

F. J. Hickernell, What Affects the Accuracy of Quasi-Monte Carlo Quadrature?, pp.16-55, 1998.
DOI : 10.1007/978-3-642-59657-5_2

F. J. Hickernell, Obtaining O(N - 2+???) Convergence for Lattice Quadrature Rules, pp.274-289, 2000.
DOI : 10.1007/978-3-642-56046-0_18

F. J. Hickernell, Error analysis for quasi-Monte Carlo methods, p.2016, 2017.

F. J. Hickernell, C. Lemieux, and A. B. Owen, Control Variates for Quasi-Monte Carlo, Statistical Science, vol.20, issue.1, pp.1-31, 2005.
DOI : 10.1214/088342304000000468

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.8.2254

H. S. Hong and F. H. Hickernell, Algorithm 823, ACM Transactions on Mathematical Software, vol.29, issue.2, pp.95-109, 2003.
DOI : 10.1145/779359.779360

J. Imai and K. S. Tan, Enhanced quasi-Monte Carlo methods with dimension reduction, Proceedings of the Winter Simulation Conference, pp.1502-1510, 2002.
DOI : 10.1109/WSC.2002.1166425

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.89.1574

J. Imai and K. S. Tan, A general dimension reduction technique for derivative pricing, The Journal of Computational Finance, vol.10, issue.2, pp.129-155, 2006.
DOI : 10.21314/JCF.2006.143

S. Joe and F. Y. Kuo, Constructing Sobol Sequences with Better Two-Dimensional Projections, SIAM Journal on Scientific Computing, vol.30, issue.5, pp.2635-2654, 2008.
DOI : 10.1137/070709359

URL : http://researchcommons.waikato.ac.nz/bitstream/10289/967/1/Joe%20constructing.pdf

L. 'ecuyer and P. , Quasi-Monte Carlo methods in finance, Proceedings of the 2004 Winter Simulation Conference, 2004.

L. 'ecuyer and P. , Quasi-Monte Carlo methods with applications in finance, Finance and Stochastics, vol.13, issue.3, pp.307-349, 2009.

L. 'ecuyer and P. , SSJ: Stochastic simulation in Java, 2016.

L. 'ecuyer, P. Demers, V. Tuffin, and B. , Rare-events, splitting, and quasi-Monte Carlo, ACM Transactions on Modeling and Computer Simulation, vol.17, issue.2 9, 2007.

L. 'ecuyer, P. Lécot, C. L-'archevêque-gaudet, and A. , On array-RQMC for Markov chains: Mapping alternatives and convergence rates, pp.485-500, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00392299

L. 'ecuyer, P. Lécot, C. Tuffin, and B. , A Randomized Quasi-Monte Carlo Simulation Method for Markov Chains, Operations Research, vol.56, issue.4, pp.958-975, 2008.
DOI : 10.1287/opre.1080.0556

URL : https://hal.archives-ouvertes.fr/inria-00070462

L. 'ecuyer, P. Lemieux, and C. , Variance Reduction via Lattice Rules, Management Science, vol.46, issue.9, pp.1214-1235, 2000.
DOI : 10.1287/mnsc.46.9.1214.12231

L. 'ecuyer, P. Lemieux, and C. , Recent advances in randomized quasi-Monte Carlo methods, Modeling Uncertainty: An Examination of Stochastic Theory, Methods, and Applications, pp.419-474, 2002.

L. 'ecuyer, P. Munger, and D. , On figures of merit for randomly-shifted lattice rules, pp.133-159, 2010.

L. 'ecuyer, P. Munger, and D. , Algorithm 958: Lattice builder: A general software tool for constructing rank-1 lattice rules, ACM Trans. on Mathematical Software, vol.42, issue.2, p.15, 2016.

L. 'ecuyer, P. Munger, D. Lécot, C. Tuffin, and B. , Sorting methods and convergence rates for array-rqmc: Some empirical comparisons, Mathematics and Computers in Simulation, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01398912

L. 'ecuyer, P. Munger, D. Tuffin, and B. , On the distribution of integration error by randomly-shifted lattice rules, Electronic Journal of Statistics, vol.4, pp.950-993, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00793293

L. 'ecuyer, P. Sanvido, and C. , Coupling from the past with randomized quasi-Monte Carlo, Mathematics and Computers in Simulation, vol.81, issue.3, pp.476-489, 2010.

L. 'ecuyer, P. Simard, and R. , Inverting the symmetrical beta distribution, ACM Transactions on Mathematical Software, vol.32, issue.4, pp.509-520, 2006.
DOI : 10.1145/1186785.1186786

C. Lemieux, Monte Carlo and Quasi-Monte Carlo Sampling, 2009.

C. Lemieux, M. Cieslak, and K. Luttmer, RandQMC User's Guide: A Package for Randomized Quasi-Monte Carlo Methods in C Software user's guide, 2004.

C. Lemieux and P. L-'ecuyer, Randomized Polynomial Lattice Rules for Multivariate Integration and Simulation, SIAM Journal on Scientific Computing, vol.24, issue.5, pp.1768-1789, 2003.
DOI : 10.1137/S1064827501393782

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.7986

R. Liu and A. B. Owen, Estimating Mean Dimensionality of Analysis of Variance Decompositions, Journal of the American Statistical Association, vol.101, issue.474, pp.712-721, 2006.
DOI : 10.1198/016214505000001410

W. L. Loh, On the asymptotic distribution of scrambled net quadrature, The Annals of Statistics, vol.31, issue.4, pp.1282-1324, 2003.
DOI : 10.1214/aos/1059655914

D. B. Madan, P. P. Carr, and E. C. Chang, The Variance Gamma Process and Option Pricing, Review of Finance, vol.2, issue.1, pp.79-105, 1998.
DOI : 10.1023/A:1009703431535

T. A. Mara and J. O. Rakoto, Comparison of some efficient methods to evaluate the main effect of computer model factors, Journal of Statistical Computation and Simulation, vol.1, issue.2, pp.167-178, 2008.
DOI : 10.1016/S0378-7788(00)00127-4

URL : https://hal.archives-ouvertes.fr/hal-01093033

J. Matous?k, On theL2-Discrepancy for Anchored Boxes, Journal of Complexity, vol.14, issue.4, pp.527-556, 1998.
DOI : 10.1006/jcom.1998.0489

J. Matou?ek, Geometric Discrepancy: An Illustrated Guide, 1999.
DOI : 10.1007/978-3-642-03942-3

H. Niederreiter, Random Number Generation and Quasi-Monte Carlo Methods, SIAM CBMS- NSF Reg. Conf. Series in Applied Mathematics, 1992.
DOI : 10.1137/1.9781611970081

H. Niederreiter and C. Xing, Nets, (t, s)-Sequences, and Algebraic Geometry, Lecture Notes in Statistics, vol.138, pp.267-302, 1998.
DOI : 10.1007/978-1-4612-1702-2_6

D. Nuyens, The construction of good lattice rules and polynomial lattice rules, Uniform Distribution and Quasi-Monte Carlo Methods: Discrepancy, Integration and Applications, pp.223-255, 2014.
DOI : 10.1515/9783110317930.223

A. B. Owen, Monte Carlo Variance of Scrambled Net Quadrature, SIAM Journal on Numerical Analysis, vol.34, issue.5, pp.1884-1910, 1997.
DOI : 10.1137/S0036142994277468

A. B. Owen, Scrambled net variance for integrals of smooth functions, The Annals of Statistics, vol.25, issue.4, pp.1541-1562, 1997.
DOI : 10.1214/aos/1031594731

A. B. Owen, Latin supercube sampling for very high-dimensional simulations, ACM Transactions on Modeling and Computer Simulation, vol.8, issue.1, pp.71-102, 1998.
DOI : 10.1145/272991.273010

A. B. Owen, Variance with alternative scramblings of digital nets, ACM Transactions on Modeling and Computer Simulation, vol.13, issue.4, pp.363-378, 2003.
DOI : 10.1145/945511.945518

A. B. Owen, Better estimation of small sobol' sensitivity indices, ACM Transactions on Modeling and Computer Simulation, vol.23, issue.2, p.11, 2013.
DOI : 10.1145/2457459.2457460

URL : http://arxiv.org/abs/1204.4763

I. H. Sloan and S. Joe, Lattice methods for multiple integration, Journal of Computational and Applied Mathematics, vol.12, issue.13, 1994.
DOI : 10.1016/0377-0427(85)90012-3

URL : http://doi.org/10.1016/0377-0427(85)90012-3

I. H. Sloan and H. Wo´zniakowskiwo´zniakowski, When Are Quasi-Monte Carlo Algorithms Efficient for High Dimensional Integrals?, Journal of Complexity, vol.14, issue.1, pp.1-33, 1998.
DOI : 10.1006/jcom.1997.0463

URL : http://doi.org/10.1006/jcom.1997.0463

I. H. Sloan and H. Wo´zniakowskiwo´zniakowski, Tractability of Multivariate Integration for Weighted Korobov Classes, Journal of Complexity, vol.17, issue.4, pp.697-721, 2001.
DOI : 10.1006/jcom.2001.0599

I. M. Sobol-', On the distribution of points in a cube and the approximate evaluation of integrals, USSR Computational Mathematics and Mathematical Physics, vol.7, issue.4, pp.86-112, 1967.
DOI : 10.1016/0041-5553(67)90144-9

I. M. Sobol-', Sensitivity indices for nonlinear mathematical models, Mathematical Modeling and Computational Experiment, vol.1, pp.407-414, 1993.

I. M. Sobol-' and E. E. Myshetskaya, Monte Carlo estimators for small sensitivity indices, Monte Carlo Methods and Applications, vol.13, pp.5-6, 2007.

S. Tezuka, Uniform Random Numbers: Theory and Practice, Kluwer Academic, 1995.
DOI : 10.1007/978-1-4615-2317-8

S. D. Tribble and A. B. Owen, Construction of weakly CUD sequences for MCMC sampling, Electronic Journal of Statistics, vol.2, issue.0, pp.634-660, 2008.
DOI : 10.1214/07-EJS162

C. Wächter and A. Keller, Efficient simultaneous simulation of Markov chains, pp.669-684, 2006.