A multivariate finite mixture latent trajectory model with application to dementia studies

DOI10.1080/02664763.2016.1141181OpenAlexW2340634132WikidataQ37252615 ScholiaQ37252615MaRDI QIDQ5138186

Tatiana Foroud, Daniel Koller, Sujuan Gao, Dongbing Lai, Huiping Xu

Publication date: 3 December 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc5021196

zbMATH Keywords

dementiacognitive declinemultivariate finite mixture latent trajectory

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Bayesian item response modeling. Theory and applications.
• Estimating the dimension of a model
• Evaluating latent class analysis models in qualitative phenotype identification
• Analyzing Microarray Gene Expression Data
• Estimating a Treatment Effect from Multidimensional Longitudinal Data
• Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm
• Beyond SEM: General Latent Variable Modeling
• Longitudinal mixed-effects models for latent cognitive function
• A Nonlinear Model with Latent Process for Cognitive Evolution Using Multivariate Longitudinal Data
• General growth mixture modeling for randomized preventive interventions

This page was built for publication: A multivariate finite mixture latent trajectory model with application to dementia studies