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Assessing the Quality of Pseudo-Random Number Generators

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Abstract

In this article, we describe a new yet simple statistical procedure to better assess the quality of pseudo-random number generators. The new procedure builds on the statistical test suite proposed by the National Institute of Standards and Technology (NIST) and is especially useful for applications in economics. Making use of properties of the binomial distribution, we estimate the conjoint significance level of the test. We apply the proposed procedure to several well-known pseudo-random number generators, and the results confirm its effectiveness.

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References

  • Ali M. M. (2007) Synthesis of the β-distribution as an aid to stochastic global optimization. Computational Statistics and Data Analysis 52(1): 133–149

    Article  Google Scholar 

  • Blum L., Blum M., Shub M. (1986) A simple unpredictable pseudo random number generator. SIAM Journal on Computing 15: 364–383

    Article  Google Scholar 

  • Diks C., Hommes C., Panchenko V., van der Weide R. (2008) E&F chaos: A user friendly software package for nonlinear economic dynamics. Computational Economics 32(1–2): 221–244

    Article  Google Scholar 

  • Geyer C. L., Williamson P. P. (2004) Detecting fraud in data sets using Benfords law. Communications in Statistics—Simulation and Computation 33(1): 229–246

    Article  Google Scholar 

  • Glaser D. N. (1999) The controversy of significance testing: Misconceptions and alternatives. American Journal of Critical Care 8(5): 291–296

    Google Scholar 

  • Information Security Institute. (2008). Crypt-X. Technical report, Queensland University of Technology, Australia. Accessed February, 2008, from http://www.isi.qut.edu.au/resources/cryptx/tests.php

  • Johnson N. L., Kotz S., Kemp A. W. (1992) Univariate discrete distributions. Wiley, New York, NY

    Google Scholar 

  • Kimbrough S. O., Murphy F. H. (2009) Learning to collude tacitly on production levels by oligopolistic agents. Computational Economics 33(1): 47–78

    Article  Google Scholar 

  • Knuth D.E. (1998) The art of computer programming (3rd Edn., Vol. 2, Seminumerical Algorithms. Addison-Wesley Series in Computer Science and Information.). Addison-Wesley Longman Publishing Co., Inc., Boston, MA

    Google Scholar 

  • Kolsrud D. O. (2008) Stochastic ceteris paribus simulations. Computational Economics 31(1): 21–43

    Article  Google Scholar 

  • Kundu D., Gupta R. D. (2007) A convenient way of generating gamma random variables using generalized exponential distribution. Computational Statistics and Data Analysis 51(6): 2796–2802

    Article  Google Scholar 

  • Lima E. J. A., Tabak B. M. (2009) Tests of random walk: A comparison of bootstrap approaches. Computational Economics 34(4): 365–382

    Article  Google Scholar 

  • Marks N. B. (2007) Kolmogorov–Smirnov test statistic and critical values for the Erlang-3 and Erlang-4 distributions. Journal of Applied Statistics 34(8): 899–906

    Google Scholar 

  • Marsaglia, G. (1995). The Marsaglia random number CDROM with the DIEHARD battery of test of randomness. Technical report, Center for Information Security and Cryptography, HKU. Accessed March, 2010. http://i.cs.hku.hk/~diehard/cdrom/

  • Maschek M. K. (2010) Intelligent mutation rate control in an economic application of genetic algorithms. Computational Economics 35(1): 25–49

    Article  Google Scholar 

  • Rukhin, A., Soto, J., Nechvatal, J., Smid, M., Barker, E., Leigh, S., Levenson, M., Vangel, M., Banks, D., Heckert, A., Dray, J., Vo, S. (2001). A statistical test suite for random and pseudorandom number generators for cryptographic applications. Technical report, NIST Special Publication 800-22 (with revisions dated May 15, 2001). Accessed March, 2010. http://csrc.nist.gov/groups/ST/toolkit/rng/documents/SP800-22b.pdf

  • Sanabria F., Killeen P. R. (2007) Better statistics for better decisions: Rejecting null hypotheses statistical tests in favor of replication statistics. Psychology in the Schools 44(5): 471–481

    Article  Google Scholar 

  • Wichmann B. A., Hill I. D. (2006) Generating good pseudo-random numbers. Computational Statistics and Data Analysis 51(3): 1614–1622

    Article  Google Scholar 

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Authors and Affiliations

  1. Departamento de Estatística, Universidade Federal de Minas Gerais, Belo Horizonte-MG, 31270-901, Brazil

    P. C. S. Luizi & F. R. B. Cruz

  2. Departamento de Computação, Universidade Federal de Ouro Preto, Ouro Preto-MG, 35400-000, Brazil

    J. van de Graaf

Authors
  1. P. C. S. Luizi
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  2. F. R. B. Cruz
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  3. J. van de Graaf
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Correspondence to F. R. B. Cruz.

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Luizi, P.C.S., Cruz, F.R.B. & van de Graaf, J. Assessing the Quality of Pseudo-Random Number Generators. Comput Econ 36, 57–67 (2010). https://doi.org/10.1007/s10614-010-9210-6

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  • DOI: https://doi.org/10.1007/s10614-010-9210-6

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