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10. Panneton, F., L'Ecuyer, P., Matsumoto, M.: Improved long-period generators
based on linear recurrences modulo 2. ACM Transactions on Mathematical Soft-
ware 32(1), 1-16 (2006)
11. Hickernell, F.J.: A generalized discrepancy and quadrature error bound. Mathe-
matics of Computation 67, 299-322 (1998)
12. Hickernell, F.J.: What affects the accuracy of quasi-Monte Carlo quadrature? In:
Niederreiter, H., Spanier, J. (eds.) Monte Carlo and Quasi-Monte Carlo Methods
1998, pp. 16-55. Springer, Berlin (2000)
13. L'Ecuyer, P., Lemieux, C.: Recent advances in randomized quasi-Monte Carlo
methods. In: Dror, M., L'Ecuyer, P., Szidarovszky, F. (eds.) Modeling Uncertainty:
An Examination of Stochastic Theory, Methods, and Applications, pp. 419-474.
Kluwer Academic, Boston (2002)
14. L'Ecuyer, P., Lecot, C., Tun, B.: A randomized quasi-Monte Carlo simulation
method for Markov chains. Operations Research (to appear, 2008)
15. Hickernell, F.J., Sloan, I.H., Wasilkowski, G.W.: On strong tractability of weighted
multivariate integration. Mathematics of Computation 73(248), 1903-1911 (2004)
16. Ben-Ameur, H., L'Ecuyer, P., Lemieux, C.: Combination of general antithetic
transformations and control variables. Mathematics of Operations Research 29(4),
946-960 (2004)
17. L'Ecuyer, P., Lemieux, C.: Variance reduction via lattice rules. Management Sci-
ence 46(9), 1214-1235 (2000)
18. Owen, A.B.: Latin supercube sampling for very high-dimensional simulations. ACM
Transactions on Modeling and Computer Simulation 8(1), 71-102 (1998)
19. Cranley, R., Patterson, T.N.L.: Randomization of number theoretic methods for
multiple integration. SIAM Journal on Numerical Analysis 13(6), 904-914 (1976)
20. Sloan, I.H., Joe, S.: Lattice Methods for Multiple Integration. Clarendon Press,
Oxford (1994)
21. L'Ecuyer, P., Lemieux, C.: Quasi-Monte Carlo via linear shift-register sequences.
In: Proceedings of the 1999 Winter Simulation Conference, pp. 632-639. IEEE
Press, Los Alamitos (1999)
22. Matousek, J.: Geometric Discrepancy: An Illustrated Guide. Springer, Berlin
(1999)
23. Liu, R., Owen, A.B.: Estimating mean dimensionality (manuscript, 2003)
24. Wang, X., Sloan, I.H.: Why are high-dimensional finance problems often of low
effective dimension? SIAM Journal on Scientific Computing 27(1), 159-183 (2005)
25. Caflisch, R.E., Morokoff, W., Owen, A.: Valuation of mortgage-backed securities
using Brownian bridges to reduce effective dimension. The Journal of Computa-
tional Finance 1(1), 27-46 (1997)
26. Avramidis, T., L'Ecuyer, P.: Ecient Monte Carlo and quasi-Monte Carlo option
pricing under the variance-gamma model. Management Science 52(12), 1930-1944
(2006)
27. Glasserman, P.: Monte Carlo Methods in Financial Engineering. Springer, New
York (2004)
28. Imai, J., Tan, K.S.: A general dimension reduction technique for derivative pricing.
Journal of Computational Finance 10(2), 129-155 (2006)
29. Morokoff, W.J.: Generating quasi-random paths for stochastic processes. SIAM
Review 40(4), 765-788 (1998)
30. Wang, X., Sloan, I.H.: Brownian bridge and principal component analysis: Toward
removing the curse of dimensionality. IMA Journal of Numerical Analysis 27, 631-
654 (2007)
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