Parametric inference for stochastic differential equations with random effects in the drift coefficient

  • Authors

    • Alsukaini Mohammed Sari huazhong university
    • Wang Xiang-Jun huazhong university
    2016-07-31
    https://doi.org/10.14419/ijams.v4i2.6328
  • Stochastic Differential Equations, Maximum Likelihood Estimator, Linear Random Effects, Fisher Information Matrix, Asymptotic Normality, Consistency.

  • Abstract

    In this paper we focus on estimating the parameters in the stochastic differential equations (SDE’s) with drift coefficients depending linearly on a random variables  and  .The distributions of the random effects  and  are depends on unknown parameters from the continuous observations of the independent processes . When  is an unknown parameter or restrict positive constant also studied in this paper. We propose the Gaussian distribution for the random effect  and the exponential distribution for the random effect    , we obtained an explicit formulas for the likelihood functions in each case and find the maximum likelihood estimators of the unknown parameters in the random effects and for the unknown parameter    . Consistency and asymptotic normality are studied just when  is normal random effect and  is constant.

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  • How to Cite

    Sari, A. M., & Xiang-Jun, W. (2016). Parametric inference for stochastic differential equations with random effects in the drift coefficient. International Journal of Advanced Mathematical Sciences, 4(2), 21-29. https://doi.org/10.14419/ijams.v4i2.6328