کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
415950 | 681263 | 2011 | 19 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Practical estimation of high dimensional stochastic differential mixed-effects models Practical estimation of high dimensional stochastic differential mixed-effects models](/preview/png/415950.png)
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.
Journal: Computational Statistics & Data Analysis - Volume 55, Issue 3, 1 March 2011, Pages 1426–1444