A review on estimation of stochastic differential equations for pharmacokinetic/pharmacodynamic models

Background & Objective: In this work, we focus on estimating the parameters of the widely used Gompertz tumor growth model, based on measurements of the tumor’s volume.. Being

Quite remarkably, the trade-o between model and data discrepancy is formu- lated and solved by using Optimal Control Theory and this work is one of the rst use of this theory

With- out any assumption made on the discrete state dynamic, [1] and [2] have proposed state estimation for discrete- time linear switching systems affected by bounded noises..

Classical approaches for getting high weak order numerical schemes for stochastic differential equations are based on weak Taylor ap- proximation or Runge-Kutta type methods [8,

On the one hand, we follow the strategy of the above mentioned paper; but on the other hand our analysis greatly simplifies the argument since it relies on the compactness result

Filamentous structures resembling viral nucleocapsids (measles) have also been described in otosclerotic lesions from two patients (McKenna et al., 1986).. Considering the

In this paper we are concerned with the application of the Kalman filter and smoothing methods to the dynamic factor model in the presence of missing observations. We assume

Abstract - This paper discusses the restricted maximum likelihood (REML) approach for the estimation of covariance matrices in linear stochastic models, as implemented