On the modeling of stationary sequences using the inverse distribution function
Sibirskie èlektronnye matematičeskie izvestiâ, Tome 19 (2022) no. 2, pp. 502-516.

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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.
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N. S. Arkashov. On the modeling of stationary sequences using the inverse distribution function. Sibirskie èlektronnye matematičeskie izvestiâ, Tome 19 (2022) no. 2, pp. 502-516. http://geodesic.mathdoc.fr/item/SEMR_2022_19_2_a17/

[1] S.M. Prigarin, Numerical modelling of random processes and fields, Inst. of Comp. Math. and Math. Geoph. Publ., Novosibirsk, 2005 | MR

[2] V.A. Ogorodnikov, S.M. Prigarin, Numerical modelling of random processes and fields: algorithms and applications, VSP, Utrecht, 1996 | MR | Zbl

[3] M.S. Taqqu, “Weak convergence to fractional Brownian motion and to the Rosenblatt process”, Z. Wahrscheinlichkeitstheor. Verw. Geb., 31:4 (1975), 287–302 | DOI | MR | Zbl

[4] P. Breuer, P. Major, “Central limit theorems for non-linear functionals of Gaussian fields”, J. Multivariate Anal., 13:3 (1983), 425–441 | DOI | MR | Zbl

[5] F.M. Dostoevsky, Crime and punishment, Collected Works in 15 volumes, v. V, Nauka-Leningrad branch, L., 1989

[6] W. Feller, An introduction to probability theory and its applications, v. II, John Wiley Sons, New York etc, 1971 | MR | Zbl

[7] A.N. Shiryaev, Probability, Springer-Verlag, New York, 1995 | MR | Zbl

[8] W. Whitt, “Bivariate distributions with given marginals”, Ann. Stat., 4 (1976), 1280–1289 | DOI | MR | Zbl

[9] N.S. Arkashov, “On a method for the probability and statistical analysis of the density of low frequency turbulent plasma”, Comput. Math. Math. Phys., 59:3 (2019), 402–413 | DOI | MR | Zbl

[10] C. Meyer, “The bivariate normal copula”, Commun. Stat., Theory Methods, 42:13 (2013), 2402–2422 | DOI | MR | Zbl

[11] A.N. Kolmogorov, “Wienersche Spiralen und einige andere interessante Kurven im Hilbertschen Raum”, C. R. (Dokl.) Acad. Sci. URSS, n. Ser., 26 (1940), 115–118 | MR | Zbl

[12] A.A. Borovkov, A.A. Mogul'skii, A.I. Sakhanenko, “Limit theorems for random processes”, Probability theory - 7, Itogi Nauki Tekh., Ser. Sovrem. Probl. Mat., Fundam. Napravleniya, 82, eds. Sakharova V.P. (ed.) et al., VINITI, M., 1995 | MR | Zbl

[13] T. Konstantopoulos, A. Sakhanenko, “Convergence and convergence rate to fractional Brownian motion for weighted random sums”, Sib. Èlektron. Mat. Izv., 1 (2004), 47–63 | MR | Zbl

[14] I.A. Ibragimov, Yu.V. Linnik, Independent and stationary sequences of random variables, Wolters-Noordhoff Publishing Company, Groningen, 1971 | MR | Zbl

[15] N.S. Arkashov, “The principle of invariance in the Donsker form to the partial sum processes of finite order moving averages”, Sib. Èlektron. Mat. Izv., 16 (2019), 1276–1288 | DOI | MR | Zbl

[16] G. Samorodnitsky, M. Taqqu, Stable non-Gaussian random processes: stochastic models with infinite variance, Chapman Hall, New York, 1994 | MR | Zbl

[17] J.M. Hughes, N.J. Foti, D.C. Krakauer, D.N. Rockmore, “Quantitative patterns of stylistic influence in the evolution of literature”, PNAS, 109:20 (2012), 7682–7686 | DOI

[18] M.J. Cannon, D.B. Percival, D.C. Caccia, G.M. Raymond, J.B. Bassingthwaighte, “Evaluating scaled window variance methods for estimating the Hurst coefficient of time series”, Physica A, 241:3-4 (1997), 606–626 | DOI

[19] B. Simon, $P(0)_2$ Euclidean (quantum) field theory, Princeton University Press, Princeton, 1974 | MR | Zbl

[20] M.J. Ablowitz, A.S. Fokas, Complex variables: introduction and applications, Cambridge University Press, Cambridge, 1997 | MR | Zbl

[21] W. Höffding, “Maszstabinvariante Korrelations-theorie”, Schr. math. Inst. Inst. angew. Math. Univ. Berlin, 5:3 (1940), 181–233 | MR | Zbl

[22] E.L. Lehmann, “Some concepts of dependence”, Ann. Math. Stat., 37:5 (1966), 1137–1153 | DOI | MR | Zbl

[23] V.V. Petrov, Sums of independent random variables, Springer, Berlin etc, 1975 | MR | Zbl