Many scientific fields have experienced growth in the use of stochastic differential equations
(SDEs), also known as diffusion processes, to model scientific phenomena over time. SDEs …

Continuous time dynamic models are similar to popular discrete time models such as
autoregressive cross-lagged models, but through use of stochastic differential equations can …

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 …

This book presents statistical models and methods for the analysis of longitudinal data. It
focuses on models for analyzing repeated measures of quantitative and qualitative variables …

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

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 …

Particle Methods for Stochastic Differential Equation Mixed Effects Models Page 1 Bayesian
Analysis (2021) 16, Number 2, pp. 575–609 Particle Methods for Stochastic Differential …

Let (X t) be solution of a one-dimensional McKean-Vlasov stochastic differential equation
with classical drift term V (α, x), self-stabilizing term Φ (β, x) and small noise amplitude ε. Our …

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 …