Practical Estimation of High Dimensional Stochastic Differential Mixed-Effects Models

Details Practical Estimation of High Dimensional Stochastic Differential Mixed-Effects Models Practical Estimation of High Dimensional Stochastic Differential Mixed-Effects Models

2010-04-22

arxiv.org/abs/1004.3871v2

Statistics

Computation

Forthcoming in "Computational Statistics & Data Analysis"

Scientific paper

Stochastic differential equations (SDEs) are established tools to model 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 to model 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.

Affiliated with

Also associated with

No associations

Rating

Practical Estimation of High Dimensional Stochastic Differential Mixed-Effects Models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Practical Estimation of High Dimensional Stochastic Differential Mixed-Effects Models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Practical Estimation of High Dimensional Stochastic Differential Mixed-Effects Models will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-459026