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

A line of recent work has analyzed the behavior of the Expectation-Maximization (EM)
algorithm in the well-specified setting, in which the population likelihood is locally strongly …

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

This paper deals with a nonparametric Nadaraya–Watson (NW) estimator of the drift function
computed from independent continuous observations of a diffusion process. Risk bounds on …

We consider N independent stochastic processes (Xi (t), t∈[0, T]), i= 1,..., N, defined by a
one-dimensional stochastic differential equation, which are continuously observed …

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

Non-linear mixed models defined by stochastic differential equations (SDEs) are
considered: the parameters of the diffusion process are random variables and vary among …

We consider some general mixed-effects diffusion models, in which the observations are
made at discrete time points and include measurement errors. In these models, the …

This paper is a survey of recent contributions on estimation in stochastic differential
equations with mixed effects. These models involve N stochastic differential equations with …