<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, we introduce a new family of stochastic Lundqvist-Korf diffusion process, defined from a <italic>g</italic>-power of the Lundqvist-Korf diffusion process. First, we determine the probabilistic characteristics of the process, such as its analytic expression, the transition probability density function from the corresponding It ˆo stochastic differential equation and obtain the conditional and non-conditional mean functions. We then study the statistical inference in this process. The parameters of this process are estimated by using the maximum likelihood estimation method with discrete sampling, thus we obtain a nonlinear equation, which is achieved via the simulated annealing algorithm. Finally, the results of the paper are applied to simulated data.</p>
</abstract>

In this paper, we introduce a new family of stochastic Lundqvist-Korf diffusion process, defined from a g-power of the Lundqvist-Korf diffusion process. First, we determine the probabilistic characteristics of the process, such as its analytic expression, the transition probability density function from the corresponding It ˆo stochastic differential equation and obtain the conditional and non-conditional mean functions. We then study the statistical inference in this process. The parameters of this process are estimated by using the maximum likelihood estimation method with discrete sampling, thus we obtain a nonlinear equation, which is achieved via the simulated annealing algorithm. Finally, the results of the paper are applied to simulated data.

A <italic>γ</italic>-power stochastic Lundqvist-Korf diffusion process: Computational aspects and simulation

Department of Mathematics and Computer Science

Laboratory of Systems Modelization and Analysis for Decision Support

Moroccan Journal of Pure and Applied Analysis

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A -power stochastic Lundqvist-Korf diffusion process: Computational aspects and simulation

In this paper, we introduce a new family of stochastic Lundqvist-Korf diffusion process, defined from a g-power of the Lundqvist-Korf diffusion process. First,...

A γ-power stochastic Lundqvist-Korf diffusion process: Computational aspects and simulation

Pubblicato online: 11 ott 2022

Pagine: 364 - 374

Ricevuto: 13 ago 2022

Accettato: 24 set 2022

DOI: https://doi.org/10.2478/mjpaa-2022-0025

Parole chiave
Stochastic Lundqvist-Korf diffusion process, Maximum likelihood estimation, Simulated annealing method, Statistical inference in diffusion process, Simulation

© 2022 El Azri Abdenbi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A γ-power stochastic Lundqvist-Korf diffusion process: Computational aspects and simulation

Pubblicato online: 11 ott 2022

Pagine: 364 - 374

Ricevuto: 13 ago 2022

Accettato: 24 set 2022

DOI: https://doi.org/10.2478/mjpaa-2022-0025

Parole chiaveStochastic Lundqvist-Korf diffusion process, Maximum likelihood estimation, Simulated annealing method, Statistical inference in diffusion process, Simulation

© 2022 El Azri Abdenbi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Stochastic Lundqvist-Korf diffusion process, Maximum likelihood estimation, Simulated annealing method, Statistical inference in diffusion process, Simulation