An approximate iterative algorithm for modeling of non-Gaussian vectors with given marginal distributions and a covariance matrix
M.S. Akenteva1,2, N.A. Kargapolova1,2, V.A. Ogorodnikov1,2
1Institute of Computational Mathematics and Mathematical Geophysics of Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russia
2Novosibirsk State University, Novosibirsk, Russia
Keywords: non-Gaussian stochastic processes, stochastic modeling, marginal distributions, covariance matrix
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.
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