Practical Estimation of High Dimensional Stochastic Differential Mixed-Effects Models

Umberto Picchini; Susanne Ditlevsen

doi:10.1016/j.csda.2010.10.003

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arXiv · 1004.3871

Practical Estimation of High Dimensional Stochastic Differential Mixed-Effects Models

Umberto Picchini·Susanne Ditlevsen

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Abstract

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.

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BibTeX RIS

Umberto Picchini, Susanne Ditlevsen. 2010-10-03. Practical Estimation of High Dimensional Stochastic Differential Mixed-Effects Models. https://doi.org/10.1016/j.csda.2010.10.003

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