Segmented mixed models with random changepoints: a maximum likelihood approach with application to treatment for depression study
From MaRDI portal
Publication:4970992
DOI10.1177/1471082X13504721WikidataQ60636823 ScholiaQ60636823MaRDI QIDQ4970992
Vito M. R. Muggeo, David C. Atkins, Sona Dimidjian, Robert J. Gallop
Publication date: 8 October 2020
Published in: Statistical Modelling (Search for Journal in Brave)
changepointnonlinear mixed modelsmixed segmented regressionpsychiatric longitudinal datarandom changepoints
Related Items (4)
A heuristic, iterative algorithm for change-point detection in abrupt change models ⋮ Fitting mixed models to messy longitudinal data: a case study involving estimation of post mortem intervals ⋮ Longitudinal mixed-effects models for latent cognitive function ⋮ Multivariate piecewise joint models with random change-points for skewed-longitudinal and survival data
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- A note on bootstrapping the sample median
- Bayesian and profile likelihood change point methods for modeling cognitive function over time
- On asymptotic distribution theory in segmented regression problems - identified case
- Two Taylor-series approximation methods for nonlinear mixed models
- Minimizing Model Fitting Objectives That Contain Spurious Local Minima by Bootstrap Restarting
- Modelling Growth and Decline in Lung Function in Duchenne’s Muscular Dystrophy with an Augmented Linear Mixed Effects Model
- Hierarchical Bayes Models for the Progression of HIV Infection Using Longitudinal CD4 T-Cell Numbers
- Mixed-Effects Models in S and S-PLUS
- Change Points in the Series of T4 Counts Prior to AIDS
- Random change point models: investigating cognitive decline in the presence of missing data
- Random Changepoint Model for Joint Modeling of Cognitive Decline and Dementia
This page was built for publication: Segmented mixed models with random changepoints: a maximum likelihood approach with application to treatment for depression study
