A one‐step method for modelling longitudinal data with differential equations

DOI10.1111/bmsp.12135zbMath1409.62239OpenAlexW2797107670WikidataQ52597229 ScholiaQ52597229MaRDI QIDQ4627515

Raymond Treinen, Yueqin Hu

Publication date: 11 March 2019

Published in: British Journal of Mathematical and Statistical Psychology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/bmsp.12135

zbMATH Keywords

dynamical systemsdifferential equationsanalytic solutiondifferential equation modelsintensive longitudinal datastatistical technique

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Applications of boundary value problems involving ordinary differential equations (34B60) Applications of statistics to psychology (62P15)

Related Items (1)

ASDE

Uses Software

• R
• UCI-ml
• deSolve
• OpenMx
• diffEq

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• OpenMX 2.0: extended structural equation and statistical modeling
• An idiographic approach to estimating models of dyadic interactions with differential equations
• Fitting nonlinear ordinary differential equation models with random effects and unknown initial conditions using the stochastic approximation expectation-maximization (SAEM) algorithm
• Time delay embedding increases estimation precision of models of intraindividual variability
• Model Selection and Multimodel Inference
• Continuous time modelling with individually varying time intervals for oscillating and non‐oscillating processes

This page was built for publication: A one‐step method for modelling longitudinal data with differential equations