Estimation of distribution algorithms for the computation of innovation estimators of diffusion processes

Innovation Method is a recognized method for the estimation of parameters in diffusion processes. It is well known that the quality of the Innovation Estimator strongly depends on an adequate selection of the initial value for the parameters when a local optimization algorithm is used in its computa...

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Bibliographic Details
Published in:Mathematics and computers in simulation 2021-09, Vol.187, p.449-467
Main Authors: Arenas, Zochil González, Jimenez, Juan Carlos, Lozada-Chang, Li-Vang, Santana, Roberto
Format: Article
Language:English
Subjects:
Diffusion process
Estimation of Distribution Algorithms
Innovation Estimators
Numerical simulations
Parameter estimation
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Summary:Innovation Method is a recognized method for the estimation of parameters in diffusion processes. It is well known that the quality of the Innovation Estimator strongly depends on an adequate selection of the initial value for the parameters when a local optimization algorithm is used in its computation. Alternatively, in this paper, we use a strategy based on a modern method for solving global optimization problems, Estimation of Distribution Algorithms (EDAs). We study the feasibility of a specific EDA - a continuous version of the Univariate Marginal Distribution Algorithm (UMDAc) - for the computation of the Innovation Estimators. Through numerical simulations, we show that the considered global optimization algorithms substantially improves the effectiveness of the Innovation Estimators for different types of diffusion processes with complex nonlinear and stochastic dynamics. •Use of EDAs for the computation of Innovation Estimators of the parameters of diffusion processes.•Our proposal improves effectiveness of the Innovation Estimators for nonlinear and stochastic dynamics.•Advantageous strategy when local optimization fails or adequate initial values are not available.
ISSN:0378-4754
1872-7166
DOI:10.1016/j.matcom.2021.03.017