A combined multilevel factor analysis and covariance regression model with mixed effects in the mean and variance structure.
Bayesian inference
combined model
covariance regression
factor analysis
heteroscedasticity
multivariate multilevel data
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
15 08 2023
15 08 2023
Historique:
revised:
20
03
2023
received:
16
06
2022
accepted:
01
05
2023
medline:
18
7
2023
pubmed:
23
6
2023
entrez:
23
6
2023
Statut:
ppublish
Résumé
Li et al developed a multilevel covariance regression (MCR) model as an extension of the covariance regression model of Hoff and Niu. This model assumes a hierarchical structure for the mean and the covariance matrix. Here, we propose the combined multilevel factor analysis and covariance regression model in a Bayesian framework, simultaneously modeling the MCR model and a multilevel factor analysis (MFA) model. The proposed model replaces the responses in the MCR part with the factor scores coming from an MFA model. Via a simulation study and the analysis of real data, we show that the proposed model is quite efficient when the responses of the MCR model are not measured directly but are latent variables such as the patient experience measurements in our motivating dataset.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3128-3144Informations de copyright
© 2023 John Wiley & Sons Ltd.
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