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.
Document type :
Journal articles
Complete list of metadatas

Cited literature [27 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01305574
Contributor : Charlotte Dion <>
Submitted on : Thursday, April 21, 2016 - 1:48:06 PM
Last modification on : Monday, September 30, 2019 - 11:08:01 AM
Long-term archiving on: Tuesday, November 15, 2016 - 8:36:13 AM

File

article_mixedsde21042016.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Charlotte Dion, Simone Hermann, Adeline Samson. Mixedsde: a R package to fit mixed stochastic differential equations. The R Journal, R Foundation for Statistical Computing, 2019, 11 (1), ⟨10.32614/RJ-2019-009⟩. ⟨hal-01305574⟩

Share

Metrics

Record views

983

Files downloads

546