An Approximate Iterative Algorithm for Modeling of Non-Gaussian Vectors with Given Marginal Distributions and Covariance Matrix

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An Approximate Iterative Algorithm for Modeling of Non-Gaussian Vectors with Given Marginal Distributions and Covariance Matrix. / Akenteva, M. S.; Kargapolova, N. A.; Ogorodnikov, V. A.

в: Numerical Analysis and Applications, Том 16, № 4, 12.2023, стр. 289-298.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

BibTeX

@article{b4af77601efa4135a704f13814849696,

title = "An Approximate Iterative Algorithm for Modeling of Non-Gaussian Vectors with Given Marginal Distributions and Covariance Matrix",

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.",

keywords = "covariance matrix, marginal distributions, non-Gaussian stochastic processes, stochastic modeling",

author = "Akenteva, {M. S.} and Kargapolova, {N. A.} and Ogorodnikov, {V. A.}",

note = "The work was supported by the Russian Science Foundation (project no. 21-71-00007); https://rdcf.ru/project/21-71-00007/.",

year = "2023",

month = dec,

doi = "10.1134/S1995423923040018",

language = "English",

volume = "16",

pages = "289--298",

journal = "Numerical Analysis and Applications",

issn = "1995-4239",

publisher = "Maik Nauka-Interperiodica Publishing",

number = "4",

}

RIS

TY - JOUR

T1 - An Approximate Iterative Algorithm for Modeling of Non-Gaussian Vectors with Given Marginal Distributions and Covariance Matrix

AU - Akenteva, M. S.

AU - Kargapolova, N. A.

AU - Ogorodnikov, V. A.

N1 - The work was supported by the Russian Science Foundation (project no. 21-71-00007); https://rdcf.ru/project/21-71-00007/.

PY - 2023/12

Y1 - 2023/12

N2 - 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.

AB - 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.

KW - covariance matrix

KW - marginal distributions

KW - non-Gaussian stochastic processes

KW - stochastic modeling

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85178950364&origin=inward&txGid=b5150e18d32a9672180d7eb98899fc64

UR - https://www.mendeley.com/catalogue/3d0f7ed8-4eab-353d-bc9f-4e0c7fb86dc9/

U2 - 10.1134/S1995423923040018

DO - 10.1134/S1995423923040018

M3 - Article

VL - 16

SP - 289

EP - 298

JO - Numerical Analysis and Applications

JF - Numerical Analysis and Applications

SN - 1995-4239

IS - 4

ER -

ID: 59388505