The DOI matches its Crossref record.
Random Number Generation and Quasi-Monte Carlo Methods.
Abstract
Preface 1. Monte Carlo methods and Quasi-Monte Carlo methods 2. Quasi-Monte Carlo methods for numerical integration 3. Low-discrepancy point sets and sequences 4. Nets and (t,s)-sequences 5. Lattice rules for numerical integration 6. Quasi- Monte Carlo methods for optimization 7. Random numbers and pseudorandom numbers 8. Nonlinear congruential pseudorandom numbers 9. Shift-Register pseudorandom numbers 10. Pseudorandom vector generation Appendix A. Finite fields and linear recurring sequences Appendix B. Continued fractions Bibliography Index.
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Cited by
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Štrumbelj, E., & Kononenko, I. (2014) · Knowledge and Information Systems · doi:10.1007/s10115-013-0679-x · Cited by 2,286
TestU01: A C library for empirical testing of random number generators
L'Ecuyer, P., & Simard, R. (2007) · ACM Transactions on Mathematical Software · doi:10.1145/1268776.1268777 · Cited by 1,014
Bhat, C. R. (2003) · Transportation Research Part B: Methodological · doi:10.1016/s0191-2615(02)00090-5 · Cited by 855
Monte Carlo methods for security pricing
Boyle, P., Broadie, M., & Glasserman, P. (1997) · Journal of Economic Dynamics and Control · doi:10.1016/s0165-1889(97)00028-6 · Cited by 854
Analysis of variance designs for model output
Jansen, M. J. W. (1999) · Computer Physics Communications · doi:10.1016/s0010-4655(98)00154-4 · Cited by 650
Metadata
- Authors
- Collings, B. J., & Niederreiter, H.
- Venue
- Journal of the American Statistical Association
- Publisher
- JSTOR
- Published
- 1993-06
- Volume
- 88
- Issue
- 422
- Pages
- 699
- ISSN
- 0162-1459 (ISSN-L)
- OpenAlex
- W2043419861
- Cited by
- 2,884
- References
- 0
- FWCI
- 53.70
- Type
- journal article
- Language
- en
- Open access
- closed
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- Collings, B. J., & Niederreiter, H. (1993). Random Number Generation and Quasi-Monte Carlo Methods. Journal of the American Statistical Association. https://doi.org/10.2307/2290359
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
