 Estimation of distribution algorithms for the computation of innovation estimators of diffusion processesZochil González Arenas, Juan Carlos Jimenez, Li-Vang Lozada-Chang and Roberto Santana Mathematics and Computers in Simulation (MATCOM), 2021, vol. 187, issue C, 449-467 Abstract: 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. Keywords: Innovation Estimators; Estimation of Distribution Algorithms; Parameter estimation; Diffusion process; Numerical simulations (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:187:y:2021:i:c:p:449-467 DOI: 10.1016/j.matcom.2021.03.017 Access Statistics for this article Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier |
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