M. S. Akenteva, N. A. Kargapolova, V. A. Ogorodnikov
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引用次数: 0
Abstract
A new iterative method for modeling of non-Gaussian random vectors with given marginal distributions and a covariance matrix is proposed in this paper. The algorithm is compared with another iterative algorithm for modeling of non-Gaussian vectors, based on reordering of a sample of independent random variables with given marginal distributions. Our numerical studies show that both algorithms are equivalent in terms of the accuracy of reproduction of a given covariance matrix, but the offered algorithm turns out to be more efficient in terms of memory usage and, in many cases, is faster than the other one.
期刊介绍：
Numerical Analysis and Applications is the translation of Russian periodical Sibirskii Zhurnal Vychislitel’noi Matematiki (Siberian Journal of Numerical Mathematics) published by the Siberian Branch of the Russian Academy of Sciences Publishing House since 1998.
The aim of this journal is to demonstrate, in concentrated form, to the Russian and International Mathematical Community the latest and most important investigations of Siberian numerical mathematicians in various scientific and engineering fields.
The journal deals with the following topics: Theory and practice of computational methods, mathematical physics, and other applied fields; Mathematical models of elasticity theory, hydrodynamics, gas dynamics, and geophysics; Parallelizing of algorithms; Models and methods of bioinformatics.
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