JOURNAL OF MULTIVARIATE ANALYSIS | 卷:174 |
An innovative strategy on the construction of multivariate multimodal linear mixed-effects models | |
Article | |
Mandiyeh, Zahra1  Kazemi, Iraj1  | |
[1] Univ Isfahan, Dept Stat, Esfahan 81746, Iran | |
关键词: Clustered random effects; ECM algorithm; Low-back pain; MCMC; Multimodality; Multiple longitudinal data; | |
DOI : 10.1016/j.jmva.2019.104533 | |
来源: Elsevier | |
【 摘 要 】
This paper presents an attractive extension of multivariate mixed-effects models to allow the modeling of correlated responses. By initiating a new multivariate multimodal distribution, the proposed strategy takes multimodality and the asymmetric structure into account in a flexible way. It can also accommodate clustered random effects on multiple longitudinal responses when data comprise various hidden sub-populations that are not directly identifiable. We introduce an explicit stochastic hierarchical representation of the proposed model to render its theoretical properties straightforward and to carry out estimation processes easily. A fully Bayesian approach is proposed to compute posterior distributions using MCMC techniques in modeling multivariate longitudinal data. Moreover, we present an EM-based maximum likelihood estimation procedure. To facilitate Bayesian computation, the estimation process of mixed models utilizes a data augmentation scheme. We analyze two real-life data on the low-back pain study and the height of school-girls to illustrate the usefulness of our proposed model in practical applications. (C) 2019 Elsevier Inc. All rights reserved.
【 授权许可】
Free
【 预 览 】
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