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Maximum likelihood estimation of stochastic differential equations with random effects driven by fractional Brownian motion

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  • Dai, Min
  • Duan, Jinqiao
  • Liao, Junjun
  • Wang, Xiangjun

Abstract

Stochastic differential equations and stochastic dynamics are good models to describe stochastic phenomena in real world. In this paper, we study N independent stochastic processes Xi(t) with real entries and the processes are determined by the stochastic differential equations with drift term relying on some random effects. We obtain the Girsanov-type formula of the stochastic differential equation driven by Fractional Brownian Motion through kernel transformation. Under some assumptions of the random effect, we estimate the parameter estimators by the maximum likelihood estimation and give some numerical simulations for the discrete observations. Results show that for the different H, the parameter estimator is closer to the true value as the amount of data increases.

Suggested Citation

  • Dai, Min & Duan, Jinqiao & Liao, Junjun & Wang, Xiangjun, 2021. "Maximum likelihood estimation of stochastic differential equations with random effects driven by fractional Brownian motion," Applied Mathematics and Computation, Elsevier, vol. 397(C).
    • Handle: RePEc:eee:apmaco:v:397:y:2021:i:c:s0096300320308808
    DOI: 10.1016/j.amc.2020.125927
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    References listed on IDEAS

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    1. Comte, F. & Genon-Catalot, V. & Samson, A., 2013. "Nonparametric estimation for stochastic differential equations with random effects," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2522-2551.
    2. Hu, Yaozhong & Nualart, David & Song, Xiaoming, 2008. "A singular stochastic differential equation driven by fractional Brownian motion," Statistics & Probability Letters, Elsevier, vol. 78(14), pages 2075-2085, October.
    3. Maud Delattre & Valentine Genon-Catalot & Adeline Samson, 2013. "Maximum Likelihood Estimation for Stochastic Differential Equations with Random Effects," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(2), pages 322-343, June.
    4. M.L. Kleptsyna & A. Le Breton, 2002. "Statistical Analysis of the Fractional Ornstein–Uhlenbeck Type Process," Statistical Inference for Stochastic Processes, Springer, vol. 5(3), pages 229-248, October.
    5. Maitra, Trisha & Bhattacharya, Sourabh, 2016. "On asymptotics related to classical inference in stochastic differential equations with random effects," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 278-288.
    6. Umberto Picchini & Julie Lyng Forman, 2019. "Bayesian inference for stochastic differential equation mixed effects models of a tumour xenography study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(4), pages 887-913, August.
    7. M.L. Kleptsyna & A. Le Breton & M.-C. Roubaud, 2000. "Parameter Estimation and Optimal Filtering for Fractional Type Stochastic Systems," Statistical Inference for Stochastic Processes, Springer, vol. 3(1), pages 173-182, January.
    8. Umberto Picchini & Andrea De Gaetano & Susanne Ditlevsen, 2010. "Stochastic Differential Mixed‐Effects Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(1), pages 67-90, March.
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