Parametric inference for discrete observations of diffusion processes with mixed effects

STOCHASTIC PROCESSES AND THEIR APPLICATIONS

卷:128

Article

Delattre, Maud¹  Genon-Catalot, Valentine²  Laredo, Catherine^3,4 

[1] Univ Paris Saclay, INRA, AgroParisTech, UMR MIA Paris, F-75005 Paris, France

[2] Univ Paris 05, Sorbonne Paris Cite, Lab MAP5, UMR CNRS 8145, Paris, France

[3] INRA, UR 1404, Lab MaIAGE, Jouy En Josas, France

[4] Univ Paris Saclay, INRA, MaIAGE, F-78350 Jouy En Josas, France

关键词: Discrete observations;    Estimating equations;    Mixed-effects models;    Parametric inference;    Stochastic differential equations;   

DOI  :  10.1016/j.spa.2017.08.016

来源: Elsevier

【 摘 要 】

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. (C) 2017 Elsevier B.V. All rights reserved.

【 授权许可】

Free