Parametric inference for discrete observations of diffusion processes with mixed effects

Maud Delattre 1 Valentine Genon-Catalot 2 Catherine Larédo 3
3 M.I.A., I.N.R.A.
LPMA - Laboratoire de Probabilités et Modèles Aléatoires, MIA - Unité de recherche Mathématiques et Informatique Appliquées
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.
Type de document :
Pré-publication, Document de travail
MAP5 2016-15. 2016
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https://hal.archives-ouvertes.fr/hal-01332630
Contributeur : Valentine Genon-Catalot <>
Soumis le : jeudi 16 juin 2016 - 11:47:57
Dernière modification le : mercredi 19 juillet 2017 - 16:36:29

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Maud Delattre, Valentine Genon-Catalot, Catherine Larédo. Parametric inference for discrete observations of diffusion processes with mixed effects. MAP5 2016-15. 2016. <hal-01332630>

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