Software for uniform random number generation: distinguishing the good and the bad
Proceedings of the 33nd conference on Winter simulation
Combined generators with components from different families
Mathematics and Computers in Simulation - Special issue: 3rd IMACS seminar on Monte Carlo methods - MCM 2001
Sum-discrepancy test on pseudorandom number generators
Mathematics and Computers in Simulation - Special issue: 3rd IMACS seminar on Monte Carlo methods - MCM 2001
Empirical evidence concerning AES
ACM Transactions on Modeling and Computer Simulation (TOMACS)
TestU01: A C library for empirical testing of random number generators
ACM Transactions on Mathematical Software (TOMS)
LEARNING RANDOM NUMBERS: A MATLAB ANOMALY
Applied Artificial Intelligence
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Different versions of the serial test for testing the uniformity and independence of vectors of successive values produced by a (pseudo)random number generator are studied. These tests partition the t-dimensional unit hypercube into k cubic cells of equal volume, generate n points (vectors) in this hypercube, count how many points fall in each cell, and compute a test statistic defined as the sum of values of some univariate function f applied to these k individual counters. Both overlapping and nonoverlapping vectors are considered. For different families of generators, such as linear congruential, Tausworthe, nonlinear inversive, etc., different ways of choosing these functions and of choosing k are compared, and formulas are obtained for the (estimated) sample size required to reject the null hypothesis of independent uniform random variables, as a function of the period length of the generator. For the classes of alternatives that correspond to linear generators, the most efficient tests turn out to have $k \gg n$ (in contrast to what is usually done or recommended in simulation books) and to use overlapping vectors.