Developments in pseudo‐random number generators. (8th August 2017)

• Record Type: Journal Article
• Authors: Deng, Lih‐Yuan; Bowman, Dale
• Is Part Of: Wiley interdisciplinary reviews. Volume 9:Number 5(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-08-08
• Subjects: statistical computing -- numerical analysis -- Monte Carlo methods -- random number generation; Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1404
• Languages: English
• ISSNs: 1939-5108

Abstract

Abstract : Monte Carlo simulations have become a common practice to evaluate a proposed statistical procedure, particularly when it is analytically intractable. Validity of any simulation study relies heavily on the goodness of random variate generators for some specified distributions, which in turn is based on the successful generation of independent variates from the uniform distribution. However, a typical computer‐generated pseudo‐random number generator (PRNG) is a deterministic algorithm and we know that no PRNG is capable of generating a truly random uniform sequence. Since the foundation of a simulation study is built on the PRNG used, it is extremely important to design a good PRNG. We review some recent developments on PRNGs with nice properties such as high‐dimensional equi‐distribution, efficiency, long period length, portability, and efficient parallel implementations. WIREs Comput Stat 2017, 9:e1404. doi: 10.1002/wics.1404 This article is categorized under: Algorithms and Computational Methods > Random Number Generation Abstract : We review some classical pseudo‐random number generators (PRNGs) currently used in R and recent generators that are efficient, have huge period lengths, and great statistical and empirical properties. We also discuss methods for efficiently finding PRNGs with these excellent properties and for designing PRNGs for parallel random number generation.