Stochastic differential mixed-effects models
(2010) In Scandinavian Journal of Statistics 37(1). p.67-90- Abstract
- Stochastic differential equations have been shown useful in describing random continuous time processes. Biomedical experiments often imply repeated measurements on a series of experimental units and differences between units can be represented by incorporating random effects into the model. When both system noise and random effects are considered, stochastic differential mixed-effects models ensue. This class of models enables the simultaneous representation of randomness in the dynamics of the phenomena being considered and variability between experimental units, thus providing a powerful modelling tool with immediate applications in biomedicine and pharmacokinetic/pharmacodynamic studies. In most cases the likelihood function is not... (More)
- Stochastic differential equations have been shown useful in describing random continuous time processes. Biomedical experiments often imply repeated measurements on a series of experimental units and differences between units can be represented by incorporating random effects into the model. When both system noise and random effects are considered, stochastic differential mixed-effects models ensue. This class of models enables the simultaneous representation of randomness in the dynamics of the phenomena being considered and variability between experimental units, thus providing a powerful modelling tool with immediate applications in biomedicine and pharmacokinetic/pharmacodynamic studies. In most cases the likelihood function is not available, and thus maximum likelihood estimation of the unknown parameters is not possible. Here we propose a computationally fast approximated maximum likelihood procedure for the estimation of the non-random parameters and the random effects. The method is evaluated on simulations from some famous diffusion processes and on real data sets (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4215982
- author
- Picchini, Umberto LU ; De Gaetano, Andrea and Ditlevsen, Susanne
- publishing date
- 2010
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- biomedical applications, Brownian motion with drift, CIR process, closed-form transition density expansion, Gaussian quadrature, geometric Brownian motion, maximum likelihood estimation, Ornstein–Uhlenbeck process, random parameters, stochastic differential equations
- in
- Scandinavian Journal of Statistics
- volume
- 37
- issue
- 1
- pages
- 67 - 90
- publisher
- Wiley-Blackwell
- external identifiers
-
- scopus:77949528435
- ISSN
- 1467-9469
- DOI
- 10.1111/j.1467-9469.2009.00665.x
- language
- English
- LU publication?
- no
- additional info
- A post-publication correction to some editorial typos is available as "Corrigendum" with DOI: 10.1111/j.1467-9469.2010.00692.x
- id
- 311eb276-442a-4a27-8ecd-6d0a9d0a1958 (old id 4215982)
- date added to LUP
- 2016-04-01 10:31:49
- date last changed
- 2022-02-10 03:02:11
@article{311eb276-442a-4a27-8ecd-6d0a9d0a1958,
abstract = {{Stochastic differential equations have been shown useful in describing random continuous time processes. Biomedical experiments often imply repeated measurements on a series of experimental units and differences between units can be represented by incorporating random effects into the model. When both system noise and random effects are considered, stochastic differential mixed-effects models ensue. This class of models enables the simultaneous representation of randomness in the dynamics of the phenomena being considered and variability between experimental units, thus providing a powerful modelling tool with immediate applications in biomedicine and pharmacokinetic/pharmacodynamic studies. In most cases the likelihood function is not available, and thus maximum likelihood estimation of the unknown parameters is not possible. Here we propose a computationally fast approximated maximum likelihood procedure for the estimation of the non-random parameters and the random effects. The method is evaluated on simulations from some famous diffusion processes and on real data sets}},
author = {{Picchini, Umberto and De Gaetano, Andrea and Ditlevsen, Susanne}},
issn = {{1467-9469}},
keywords = {{biomedical applications; Brownian motion with drift; CIR process; closed-form transition density expansion; Gaussian quadrature; geometric Brownian motion; maximum likelihood estimation; Ornstein–Uhlenbeck process; random parameters; stochastic differential equations}},
language = {{eng}},
number = {{1}},
pages = {{67--90}},
publisher = {{Wiley-Blackwell}},
series = {{Scandinavian Journal of Statistics}},
title = {{Stochastic differential mixed-effects models}},
url = {{https://lup.lub.lu.se/search/files/1923792/4215984}},
doi = {{10.1111/j.1467-9469.2009.00665.x}},
volume = {{37}},
year = {{2010}},
}