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A Comment on the Implementation of the Ziggurat Method

Author

Listed:
  • Leong, Philip H. W.
  • Zhang, Ganglie
  • Lee, Dong-U
  • Luk, Wayne
  • Villasenor, John

Abstract

We show that the short period of the uniform random number generator in the published implementation of Marsaglia and Tsang's Ziggurat method for generating random deviates can lead to poor distributions. Changing the uniform random number generator used in its implementation fixes this issue.

Suggested Citation

  • Leong, Philip H. W. & Zhang, Ganglie & Lee, Dong-U & Luk, Wayne & Villasenor, John, 2005. "A Comment on the Implementation of the Ziggurat Method," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i07).
    • Handle: RePEc:jss:jstsof:v:012:i07
    DOI: http://hdl.handle.net/10.18637/jss.v012.i07
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    References listed on IDEAS

    as
    1. Marsaglia, George & Tsang, Wai Wan, 2000. "The Ziggurat Method for Generating Random Variables," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 5(i08).
    2. Marsaglia, George, 2003. "Xorshift RNGs," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 8(i14).
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    Cited by:

    1. Nguyen Nguyet & Xu Linlin & Ökten Giray, 2018. "A quasi-Monte Carlo implementation of the ziggurat method," Monte Carlo Methods and Applications, De Gruyter, vol. 24(2), pages 93-99, June.
    2. repec:jss:jstsof:12:i07 is not listed on IDEAS
    3. Yiran Chen & Giray Ökten, 2022. "A goodness-of-fit test for copulas based on the collision test," Statistical Papers, Springer, vol. 63(5), pages 1369-1385, October.

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