Name
Title | ||
|---|---|---|
Practical estimation of high dimensional stochastic differential mixed-effects models |
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.csda.2010.10.003 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
practical estimation,population estimation,mixed-effects modelling,experiment variability,high dimensional stochastic differential,automatic dierentiation,automatic differentiation,standard model,stochastic differential mixed-effects model,cox–ingersoll–ross process,square root model,biomedical modelling,maximum likelihood estimation,stochastic differential equation,model dynamic,mixed-effects approach,parameter estimation method,closed form transition density expansion,mixed effects,cox ingersoll ross,mixed effects model,dynamic system,ornstein uhlenbeck,maximum likelihood estimate,statistical computing,parameter estimation | Econometrics,Density estimation,Population,Differential equation,Stochastic differential equation,Mixed model,Stochastic modelling,Estimation theory,Statistics,Mathematics,Randomness | Journal |
Volume | Issue | ISSN |
55 | 3 | Computational Statistics and Data Analysis |
Citations | PageRank | References |
3 | 0.49 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Umberto Picchini | 1 | 9 | 2.99 |
| Susanne Ditlevsen | 2 | 57 | 7.84 |