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Full-Text Articles in Statistics and Probability
A Statistical Evaluation Of Algorithms For Independently Seeding Pseudo-Random Number Generators Of Type Multiplicative Congruential (Lehmer-Class)., Robert Grisham Stewart
A Statistical Evaluation Of Algorithms For Independently Seeding Pseudo-Random Number Generators Of Type Multiplicative Congruential (Lehmer-Class)., Robert Grisham Stewart
Electronic Theses and Dissertations
To be effective, a linear congruential random number generator (LCG) should produce values that are (a) uniformly distributed on the unit interval (0,1) excluding endpoints and (b) substantially free of serial correlation. It has been found that many statistical methods produce inflated Type I error rates for correlated observations. Theoretically, independently seeding an LCG under the following conditions attenuates serial correlation: (a) simple random sampling of seeds, (b) non-replicate streams, (c) non-overlapping streams, and (d) non-adjoining streams. Accordingly, 4 algorithms (each satisfying at least 1 condition) were developed: (a) zero-leap, (b) fixed-leap, (c) scaled random-leap, and (d) unscaled random-leap. Note …