A Comparison Study of Finding Efficient Methods for Generating Normal Random Numbers

Authors

  • Anamul Haque Sajib Department of Statistics, Dhaka University, Dhaka-1000, Bangladesh
  • Syeda Fateha Akter Department of Statistics, Dhaka University, Dhaka-1000, Bangladesh

DOI:

https://doi.org/10.3329/dujs.v67i2.54579

Keywords:

Normal distribution, Monte Carlo integration, Ljung-Box test, Bootstrapping.

Abstract

Normal distribution is one of the most commonly used non-uniform distributions in applications involving simulations. Advanced computing facilities make the simulation tasks simple but the challenge is to meet the increasingly stringent requirements on the statistical quality of the generated samples. In this paper, we examine performances of different existing methods available to generate random samples from normal distribution based on statistical quality of the generated samples (randomness and normality) and computational complexities. From the simulation study, it is observed that CDF approximation based method and acceptance-rejection method devised by Rao et al12 and Sigman14 are the fastest and the slowest respectively among all algorithms considered in this paper while generated samples produced by all methods satisfy randomness and normality properties. An application involving simulation from normal distribution is shown by considering a Monte Carlo integration problem.

Dhaka Univ. J. Sci. 67(2): 91-98, 2019 (July)

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Published

2019-07-30

How to Cite

Sajib, A. H., & Akter, S. F. (2019). A Comparison Study of Finding Efficient Methods for Generating Normal Random Numbers. Dhaka University Journal of Science, 67(2), 91–98. https://doi.org/10.3329/dujs.v67i2.54579

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Articles