Special Issue in honor of John W. Neuberger.
Electron. J. Diff. Eqns., Special Issue 02 (2023), pp. 193-207.
Determining the background driving process of the Ornstein-Uhlenbeck model
Maria C. Mariani, Peter K. Asante, William Kubin,
Osei K. Tweneboah, Maria Beccar-Varela
Abstract:
In this work, we determine appropriate background driving processes for the
3-component superposed Ornstein-Uhlenbeck model by analyzing the fractal
characteristics of the data sets using the rescaled range analysis (R/S),
the detrended fluctuation analysis (DFA), and the diffusion entropy analysis (DEA).
Published March 27, 2023.
Math Subject Classifications: 34k50, 34F50, 60H10.
Key Words: Stochastic differential equation; Ito Calculus; Levy Process;
Ornstein-Uhlenbeck model; Superposed Ornstein-Uhlenbeck model;
Gaussian process; Background driving process (BDP);
Diffusion entropy analysis (DEA); long-range correlations;
detrended fluctuation analysis (DFA); rescaled range analysis (R/S).
DOI: https://doi.org/10.58997/ejde.sp.02.m1
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Maria C. Mariani
Department of Mathematical Sciences
the University of Texas at El Paso
TX 79968, USA
email: mcmariani@utep.edu
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Peter K. Asante
Computational Science Program
the University of Texas at El Paso
TX 79968, USA
email: pkasante@miners.utep.edu
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William Kubin
Computational Science Program
the University of Texas at El Paso
TX 79968, USA
email: wkubin@miners.utep.edu
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Osei K. Tweneboah
Department of Data Science
Ramapo College of New Jersey
505 Ramapo Valley Road
Mahwah, NJ 07430, USA
email: otwenebo@ramapo.edu
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Maria Beccar-Varela
Department of Mathematical Sciences
the University of Texas at El Paso
TX 79968, USA
email: mpvarela@utep.edu
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