This article is cited in 1 paper

An approximate iterative algorithm for modeling of non-Gaussian vectors with given marginal distributions and a covariance matrix

M. S. Akenteva^ab, N. A. Kargapolova^ab, V. A. Ogorodnikov<sup>ab

a</sup> Institute of Computational Mathematics and Mathematical Geophysics of Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russia
^b Novosibirsk State University, Russia

Abstract: A new iterative method for the modeling of non-Gaussian random vectors with given marginal distributions and covariance matrix is proposed in this paper. The algorithm is compared with another iterative algorithm for the modeling of non-Gaussian vectors, which is based on reordering 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 reproducing the given covariance matrix, but the proposed algorithm turns out to be more efficient in terms of memory usage and, in many cases, is faster than the other one.

Key words: non-Gaussian stochastic processes, stochastic modeling, marginal distributions, covariance matrix.

UDC: 519.6

Received: 29.03.2023
Revised: 18.05.2023
Accepted: 05.09.2023

DOI: 10.15372/SJNM20230401

 English version: Numerical Analysis and Applications, 2023, 16:4, 289–298