Critical Analysis of Beta Random Variable Generation Methods

Elena Almaraz Luengo () and Carlos Gragera
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Elena Almaraz Luengo: Department of Statistics and Operational Research, Faculty of Mathematical Science, Complutense University of Madrid, 28040 Madrid, Spain
Carlos Gragera: Faculty of Mathematical Science, Complutense University of Madrid, 28040 Madrid, Spain

Mathematics, 2023, vol. 11, issue 24, 1-31

Abstract: The fast generation of values of the beta random variable is a subject of great interest and multiple applications, ranging from purely mathematical and statistical ones to applications in management and production, among others. There are several methods for generating these values, with one of the essential points for their design being the selection of random seeds. Two interesting aspects converge here: the use of sequences as inputs (and the need for them to verify properties such as randomness and uniformity, which are verified through statistical test suites) and the design of the algorithm for the generation of the variable. In this paper, we analyse, in detail, the algorithms that have been developed in the literature, both from a mathematical/statistical and computational point of view. We also provide empirical development using R software, which is currently in high demand and is one of the main novelties with respect to previous comparisons carried out in FORTRAN. We establish which algorithms are more efficient and in which contexts, depending on the different values of the parameters, allowing the user to determine the best method given the experimental conditions.

Keywords: beta random variable; dieharder; hypothesis testing; NIST; pseudo-random number; simulation; statistical tests suite; TestU01 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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