Practical estimation of high dimensional stochastic differential mixed-effects models
(2011) In Computational Statistics & Data Analysis 55(3). p.1426-1444- Abstract
- Stochastic differential equations (SDEs) are established tools for modeling physical phenomena whose dynamics are affected by random noise. By estimating parameters of an SDE, intrinsic randomness of a system around its drift can be identified and separated from the drift itself. When it is of interest to model dynamics within a given population, i.e. to model simultaneously the performance of several experiments or subjects, mixed-effects modelling allows for the distinction of between and within experiment variability. A framework for modeling dynamics within a population using SDEs is proposed, representing simultaneously several sources of variation: variability between experiments using a mixed-effects approach and stochasticity in... (More)
- Stochastic differential equations (SDEs) are established tools for modeling physical phenomena whose dynamics are affected by random noise. By estimating parameters of an SDE, intrinsic randomness of a system around its drift can be identified and separated from the drift itself. When it is of interest to model dynamics within a given population, i.e. to model simultaneously the performance of several experiments or subjects, mixed-effects modelling allows for the distinction of between and within experiment variability. A framework for modeling dynamics within a population using SDEs is proposed, representing simultaneously several sources of variation: variability between experiments using a mixed-effects approach and stochasticity in the individual dynamics, using SDEs. These stochastic differential mixed-effects models have applications in e.g. pharmacokinetics/pharmacodynamics and biomedical modelling. A parameter estimation method is proposed and computational guidelines for an efficient implementation are given. Finally the method is evaluated using simulations from standard models like the two-dimensional Ornstein–Uhlenbeck (OU) and the square root models. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4215973
- author
- Picchini, Umberto LU and Ditlevsen, Susanne
- publishing date
- 2011
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Automatic differentiation, Closed form transition density expansion, Maximum likelihood estimation, Population estimation, Stochastic differential equation, Cox–Ingersoll–Ross process
- in
- Computational Statistics & Data Analysis
- volume
- 55
- issue
- 3
- pages
- 1426 - 1444
- publisher
- Elsevier
- external identifiers
-
- scopus:78649325422
- ISSN
- 0167-9473
- DOI
- 10.1016/j.csda.2010.10.003
- language
- English
- LU publication?
- no
- id
- 559dd93f-682f-4469-85f3-74b7b96db5a2 (old id 4215973)
- date added to LUP
- 2016-04-01 10:48:23
- date last changed
- 2022-04-04 21:23:39
@article{559dd93f-682f-4469-85f3-74b7b96db5a2,
abstract = {{Stochastic differential equations (SDEs) are established tools for modeling physical phenomena whose dynamics are affected by random noise. By estimating parameters of an SDE, intrinsic randomness of a system around its drift can be identified and separated from the drift itself. When it is of interest to model dynamics within a given population, i.e. to model simultaneously the performance of several experiments or subjects, mixed-effects modelling allows for the distinction of between and within experiment variability. A framework for modeling dynamics within a population using SDEs is proposed, representing simultaneously several sources of variation: variability between experiments using a mixed-effects approach and stochasticity in the individual dynamics, using SDEs. These stochastic differential mixed-effects models have applications in e.g. pharmacokinetics/pharmacodynamics and biomedical modelling. A parameter estimation method is proposed and computational guidelines for an efficient implementation are given. Finally the method is evaluated using simulations from standard models like the two-dimensional Ornstein–Uhlenbeck (OU) and the square root models.}},
author = {{Picchini, Umberto and Ditlevsen, Susanne}},
issn = {{0167-9473}},
keywords = {{Automatic differentiation; Closed form transition density expansion; Maximum likelihood estimation; Population estimation; Stochastic differential equation; Cox–Ingersoll–Ross process}},
language = {{eng}},
number = {{3}},
pages = {{1426--1444}},
publisher = {{Elsevier}},
series = {{Computational Statistics & Data Analysis}},
title = {{Practical estimation of high dimensional stochastic differential mixed-effects models}},
url = {{https://lup.lub.lu.se/search/files/2150302/4215974}},
doi = {{10.1016/j.csda.2010.10.003}},
volume = {{55}},
year = {{2011}},
}