This paper is a survey of existing estimation methods for pharmacokinetic/pharmacodynamic
(PK/PD) models based on stochastic differential equations (SDEs). Most parametric …

Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects
Models Mixed Effects Models for the Population Approach: Models, Tasks, Methods and …

This chapter is concerned with continuous time processes, which are often modeled as a
system of ordinary differential equations (ODEs). These models assume that the observed …

Stochastic differential equations have been shown useful in describing random continuous
time processes. Biomedical experiments often imply repeated measurements on a series of …

Models defined by stochastic differential equations (SDEs) allow for the representation of
random variability in dynamical systems. The relevance of this class of models is growing in …

Stochastic differential equations (SDEs) are established tools for modeling physical
phenomena whose dynamics are affected by random noise. By estimating parameters of an …

We consider N independent stochastic processes (** (t), t∈[0, Ti]), i= 1,…, N, defined by a
stochastic differential equation with drift term depending on a random variable φi. The …

Growth curve data consist of repeated measurements of a continuous growth process over
time in a population of individuals. These data are classically analyzed by nonlinear mixed …

We introduce a stochastic model of diffeomorphisms, whose action on a variety of data types
descends to stochastic evolution of shapes, images and landmarks. The stochasticity is …

The fatigue failure of automobile structures has drawn much attention recently in reliability
and endurance design. Therefore, in order to predict the fatigue life accurately, complex …