Fitting nonlinear ordinary differential equation models with random effects and unknown initial conditions using the stochastic approximation expectation-maximization (SAEM) algorithm

DOI10.1007/s11336-014-9431-zzbMath1342.62168OpenAlexW2001940873WikidataQ35637958 ScholiaQ35637958MaRDI QIDQ736434

Andrew Sherwood, Sy-Miin Chow, Hong-Tu Zhu, Zhao-hua Lu

Publication date: 4 August 2016

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11336-014-9431-z

zbMATH Keywords

dynamiclongitudinaldifferential equationnonlinearstochastic EM

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Stochastic approximation (62L20) Applications of statistics to psychology (62P15)

Related Items (7)

A joint process model of consensus and longitudinal dynamics ⋮ Bayesian analysis of ambulatory blood pressure dynamics with application to irregularly spaced sparse data ⋮ Estimation of nonlinear mixed‐effects continuous‐time models using the continuous‐discrete extended Kalman filter ⋮ A one‐step method for modelling longitudinal data with differential equations ⋮ Representing sudden shifts in intensive dyadic interaction data using differential equation models with regime switching ⋮ Zero-inflated regime-switching stochastic differential equation models for highly unbalanced multivariate, multi-subject time-series data ⋮ Computation for latent variable model estimation: a unified stochastic proximal framework

Uses Software

• R
• SAS

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Maximum likelihood estimation in nonlinear mixed effects models
• Estimation of constant and time-varying dynamic parameters of HIV infection in a nonlinear differential equation model
• Monte Carlo maximum likelihood estimation for discretely observed diffusion processes
• Parameter estimation of partially observed continuous time stochastic processes via the EM algorithm
• Maximum likelihood estimation and model comparison for mixtures of structural equation models with ignorable missing data
• Continuous time state space modeling of panel data by means of sem
• Parameter estimation in stochastic differential equations with Markov chain Monte Carlo and non-linear Kalman filtering
• Closed-form likelihood expansions for multivariate diffusions
• Nonlinear regime-switching state-space (RSSS) models
• Analysis of SPDEs arising in path sampling. I: The Gaussian case
• Generalized score test of homogeneity for mixed effects models
• Conditional path sampling of SDEs and the Langevin MCMC method
• Estimation of parameters in incomplete data models defined by dynamical systems
• Maximum Likelihood Estimation for Spatial Models by Markov Chain Monte Carlo Stochastic Approximation
• SEM Modeling with Singular Moment Matrices Part II: ML-Estimation of Sampled Stochastic Differential Equations
• Accurate Numerical Implementation of the Continuous-Discrete Extended Kalman Filter
• Bayesian estimation of semiparametric nonlinear dynamic factor analysis models using the Dirichlet process prior
• On Identifiability of Nonlinear ODE Models and Applications in Viral Dynamics
• Exact and Computationally Efficient Likelihood-Based Estimation for Discretely Observed Diffusion Processes (with Discussion)
• Continuous time modeling of panel data: SEM versus filter techniques
• The aliasing‐phenomenon in visual terms
• Asymptotic properties of a stochastic EM Algorithm for estimating mixing proportions
• Hierarchical Bayesian Analysis of Changepoint Problems
• Parameter Estimation for Differential Equations: a Generalized Smoothing Approach
• Statistical methods for HIV dynamic studies in AIDS clinical trials
• Bayesian estimation of state-space models using the Metropolis-Hastings algorithm within Gibbs sampling.

This page was built for publication: Fitting nonlinear ordinary differential equation models with random effects and unknown initial conditions using the stochastic approximation expectation-maximization (SAEM) algorithm