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A general purpose module using refined descriptive sampling for installation in simulation systems

Author

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  • Abdelouhab Aloui
  • Arezki Zioui
  • Megdouda Ourbih-Tari
  • Amine Alioui

Abstract

Some form of random sampling is employed in all simulation studies, so, some possibly substantial sampling errors are inevitable. Therefore, a new paradigm emerged: it is not always necessary to resort to randomness to generate inputs. Then, novel sampling methods were derived from this paradigm like for example Refined Descriptive Sampling (RDS). In this paper, we propose a software component under Linux called getRDS which implements an RDS number generator of high quality using the RDS method. It was highly tested by statistical tests and compared to the well known Mersenne Twister random number generator MT19937 (MT). We noticed that getRDS has passed better all tests than MT. Some illustrations of the uniformity are also given together with its comparison with MT through an M/M/1 simulation system. The obtained results through simulation demonstrate that the software component produces an accurate point estimates of the true parameters. Moreover, the getRDS random number generator can significantly improve the performance of the M/M/1 queues compared to MT since its variance reduction is over 50 % in some cases. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Abdelouhab Aloui & Arezki Zioui & Megdouda Ourbih-Tari & Amine Alioui, 2015. "A general purpose module using refined descriptive sampling for installation in simulation systems," Computational Statistics, Springer, vol. 30(2), pages 477-490, June.
    • Handle: RePEc:spr:compst:v:30:y:2015:i:2:p:477-490
    DOI: 10.1007/s00180-014-0545-7
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    Cited by:

    1. Oualid Saci & Megdouda Ourbih-Tari & Leila Baiche, 2023. "Maximum Likelihood Estimation of Parameters of a Random Variable Using Monte Carlo Methods," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 540-571, February.

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