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Using maximum cross section method for filtering jump-diffusion random processes. / Averina, Tatyana A.; Rybakov, Konstantin A.

In: Russian Journal of Numerical Analysis and Mathematical Modelling, Vol. 35, No. 2, 01.04.2020, p. 55-67.

Research output: Contribution to journal › Article › peer-review

Harvard

Averina, TA & Rybakov, KA 2020, 'Using maximum cross section method for filtering jump-diffusion random processes', Russian Journal of Numerical Analysis and Mathematical Modelling, vol. 35, no. 2, pp. 55-67. https://doi.org/10.1515/rnam-2020-0005

APA

Averina, T. A., & Rybakov, K. A. (2020). Using maximum cross section method for filtering jump-diffusion random processes. Russian Journal of Numerical Analysis and Mathematical Modelling, 35(2), 55-67. https://doi.org/10.1515/rnam-2020-0005

Vancouver

Averina TA, Rybakov KA. Using maximum cross section method for filtering jump-diffusion random processes. Russian Journal of Numerical Analysis and Mathematical Modelling. 2020 Apr 1;35(2):55-67. doi: 10.1515/rnam-2020-0005

Author

Averina, Tatyana A. ; Rybakov, Konstantin A. / Using maximum cross section method for filtering jump-diffusion random processes. In: Russian Journal of Numerical Analysis and Mathematical Modelling. 2020 ; Vol. 35, No. 2. pp. 55-67.

BibTeX

@article{052397c2610a4686b162a3e3508fbcac,
title = "Using maximum cross section method for filtering jump-diffusion random processes",
abstract = "The paper is focused on problem of filtering random processes in dynamical systems whose mathematical models are described by stochastic differential equations with a Poisson component. The solution of a filtering problem supposes simulation of trajectories of solutions to a stochastic differential equation. The trajectory modelling procedure includes simulation of a Poisson flow permitting application of the maximum cross section method and its modification.",
keywords = "estimation, filtering, maximum cross section method, particle filter, particle method, statistical modelling, Stochastic differential equation with jumps",
author = "Averina, {Tatyana A.} and Rybakov, {Konstantin A.}",
year = "2020",
month = apr,
day = "1",
doi = "10.1515/rnam-2020-0005",
language = "English",
volume = "35",
pages = "55--67",
journal = "Russian Journal of Numerical Analysis and Mathematical Modelling",
issn = "0927-6467",
publisher = "Walter de Gruyter GmbH",
number = "2",

}

RIS

TY - JOUR

T1 - Using maximum cross section method for filtering jump-diffusion random processes

AU - Averina, Tatyana A.

AU - Rybakov, Konstantin A.

PY - 2020/4/1

Y1 - 2020/4/1

N2 - The paper is focused on problem of filtering random processes in dynamical systems whose mathematical models are described by stochastic differential equations with a Poisson component. The solution of a filtering problem supposes simulation of trajectories of solutions to a stochastic differential equation. The trajectory modelling procedure includes simulation of a Poisson flow permitting application of the maximum cross section method and its modification.

AB - The paper is focused on problem of filtering random processes in dynamical systems whose mathematical models are described by stochastic differential equations with a Poisson component. The solution of a filtering problem supposes simulation of trajectories of solutions to a stochastic differential equation. The trajectory modelling procedure includes simulation of a Poisson flow permitting application of the maximum cross section method and its modification.

KW - estimation

KW - filtering

KW - maximum cross section method

KW - particle filter

KW - particle method

KW - statistical modelling

KW - Stochastic differential equation with jumps

UR - http://www.scopus.com/inward/record.url?scp=85084728278&partnerID=8YFLogxK

U2 - 10.1515/rnam-2020-0005

DO - 10.1515/rnam-2020-0005

M3 - Article

AN - SCOPUS:85084728278

VL - 35

SP - 55

EP - 67

JO - Russian Journal of Numerical Analysis and Mathematical Modelling

JF - Russian Journal of Numerical Analysis and Mathematical Modelling

SN - 0927-6467

IS - 2

ER -

ID: 24313763