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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 intra-individual and interindividual variability in repeated experiments leading to longitudinal data. We consider N i.i.d. stochastic processes defined by a stochastic differential equation with linear mixed effects which are discretely observed. We study a parametric framework with distributions leading to explicit approximate likelihood functions and investigate the asymptotic behavior of estimators under the asymptotic framework : the number N of individuals (trajectories) and the number n of observations per individual tend to infinity within a fixed time interval. The estimation method is assessed on simulated data for various models.
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https://hal.inrae.fr/hal-02622519
Déposant : Migration Prodinra <>
Soumis le : mardi 26 mai 2020 - 05:55:03
Dernière modification le : mardi 15 septembre 2020 - 09:40:04

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Maud Delattre, Valentine Genon-Catalot, Catherine Laredo. Parametric inference for discrete observations of diffusion processes with mixed effects. Stochastic Processes and their Applications, Elsevier, 2018, 128 (6), pp.1929-1957. ⟨10.1016/j.spa.2017.08.016⟩. ⟨hal-02622519⟩

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