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Sibirskie Èlektronnye Matematicheskie Izvestiya [Siberian Electronic Mathematical Reports], 2022, Volume 19, Issue 2, Pages 502–516
DOI: https://doi.org/10.33048/semi.2022.19.042 (Mi semr1517) 		 
This article is cited in 1 scientific paper (total in 1 paper)

Probability theory and mathematical statistics

On the modeling of stationary sequences using the inverse distribution function

N. S. Arkashov

Novosibirsk State Technical University, 20, Karl Marx ave., 630073, Novosibirsk, Russia
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DOI: https://doi.org/10.33048/semi.2022.19.042
Abstract: We study a method for modeling stationary sequences, which is implemented generally speaking by a nonlinear transformation of Gaussian noise. The paper establishes limit theorems in the metric space $D[0,1]$ for normalized processes of partial sums of sequences obtained as a result of the mentioned Gaussian noise transformation. Application of this method for simulating function words in fiction is investigated.
Keywords: modeling of stationary processes, long-range dependence, limit theorems, function words in fiction.
Received September 20, 2021, published August 23, 2022
Bibliographic databases:
Document Type: Article
UDC: 519.218.8,519.214
MSC: 60F17,60G10,65C20
Language: English
Citation: N. S. Arkashov, “On the modeling of stationary sequences using the inverse distribution function”, Sib. Èlektron. Mat. Izv., 19:2 (2022), 502–516
Citation in format AMSBIB
\Bibitem{Ark22}
\by N.~S.~Arkashov
\paper On the modeling of stationary sequences using the inverse distribution function
\jour Sib. \`Elektron. Mat. Izv.
\yr 2022
\vol 19
\issue 2
\pages 502--516
\mathnet{http://mi.mathnet.ru/semr1517}
\mathscinet{https://mathscinet.ams.org/mathscinet-getitem?mr=4478143}
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