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On asymptotics related to classical inference in stochastic differential equations with random effects

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  • Maitra, Trisha
  • Bhattacharya, Sourabh

Abstract

Delattre et al. (2013) considered n independent stochastic differential equations (SDE’s), where in each case the drift term is associated with a random effect, the distribution of which depends upon unknown parameters. Assuming the independent and identical (iid) situation the authors provide independent proofs of weak consistency and asymptotic normality of the maximum likelihood estimators (MLE’s) of the hyper-parameters of their random effects parameters.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:stapro:v:110:y:2016:i:c:p:278-288
    DOI: 10.1016/j.spl.2015.10.001
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    References listed on IDEAS

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    1. 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.
    2. Maitra, Trisha & Bhattacharya, Sourabh, 2015. "On Bayesian asymptotics in stochastic differential equations with random effects," Statistics & Probability Letters, Elsevier, vol. 103(C), pages 148-159.
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    Cited by:

    1. 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).

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