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Parametric inference for discrete observations of diffusion processes with mixed effects

Abstract : Stochastic differential equations with mixed effects provide means to model intraindividual and in-terindividual variability in biomedical experiments based on longitudinal data. We consider N i.i.d. stochastic processes (Xi(t), t ∈ [0, T ]), i = 1,. .. , N , defined by a stochastic differential equation with linear mixed effects. We consider a parametric framework with distributions leading to explicit approximate likelihood functions and investigate the asymptotic behaviour of estimators under the double asymptotic framework: the number N of individuals (trajectories) and the number n of observations per individual tend to infinity within the fixed time interval [0, T ]. The estimation method is assessed on simulated data for various models comprised in our framework.
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https://hal.archives-ouvertes.fr/hal-01332630
Contributor : Valentine Genon-Catalot <>
Submitted on : Thursday, June 16, 2016 - 11:47:57 AM
Last modification on : Tuesday, May 26, 2020 - 2:32:03 AM

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  • HAL Id : hal-01332630, version 1

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Maud Delattre, Valentine Genon-Catalot, Catherine Larédo. Parametric inference for discrete observations of diffusion processes with mixed effects. 2016. ⟨hal-01332630⟩

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