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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) 		 
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}
\crossref{https://doi.org/10.33048/semi.2022.19.042}
\mathscinet{http://mathscinet.ams.org/mathscinet-getitem?mr=4478143}
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