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

Author

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  • Arenas, Zochil González
  • Jimenez, Juan Carlos
  • Lozada-Chang, Li-Vang
  • Santana, Roberto

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.

Suggested Citation

  • Arenas, Zochil González & Jimenez, Juan Carlos & Lozada-Chang, Li-Vang & Santana, Roberto, 2021. "Estimation of distribution algorithms for the computation of innovation estimators of diffusion processes," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 187(C), pages 449-467.
    • Handle: RePEc:eee:matcom:v:187:y:2021:i:c:p:449-467
    DOI: 10.1016/j.matcom.2021.03.017
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    References listed on IDEAS

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