Mixedsde: a R package to fit mixed stochastic differential equations

Abstract : Stochastic differential equations (SDEs) are useful to model continuous stochastic processes. When (independent) repeated temporal data are available, variability between the trajectories can be modeled by introducing random effects in the drift of the SDEs. These models are useful to analyse neuronal data, crack length data, pharmacokinetics, financial data, to cite some applications among other. The R package focuses on the estimation of SDEs with linear random effects in the drift. The goal is to estimate the common density of the random effects from repeated discrete observations of the SDE. The package mixedsde proposes three estimation methods: a Bayesian parametric, a frequentist parametric and a frequentist nonparametric method. The three procedures are described as well as the main functions of the package. Illustrations are presented on simulated and real data.
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Contributeur : Charlotte Dion <>
Soumis le : jeudi 21 avril 2016 - 13:48:06
Dernière modification le : jeudi 11 janvier 2018 - 06:27:32
Document(s) archivé(s) le : mardi 15 novembre 2016 - 08:36:13


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  • HAL Id : hal-01305574, version 1



Charlotte Dion, Simone Hermann, Adeline Samson. Mixedsde: a R package to fit mixed stochastic differential equations. 2016. 〈hal-01305574〉



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