A class of flexible models for analysis of complex structured correlated data with application to clustered longitudinal data. Issue 1 (7th November 2017)

• Record Type: Journal Article
• Authors: Yi, Grace Y.; He, Wenqing; Li, Haocheng
• Is Part Of: Stat. Volume 6:Issue 1(2017)
• Page Start: 448
• Page End: 461
• Publication Date: 2017-11-07
• Subjects: clustered longitudinal data -- generalized linear mixed model -- pairwise likelihood; Statistics -- Periodicals; 519.2
• DOI: 10.1002/sta4.159
• Languages: English
• ISSNs: 2049-1573

Abstract

Abstract : Generalized linear mixed models have been widely used in correlated data analysis. The applicability of these models is, however, hampered when data possess multilevel complex association structures. For instance, for longitudinal data arising in clusters, modelling complexity is a serious issue, and it is desirable to develop flexible models that are both computationally manageable and interpretatively meaningful. For these purposes, we propose a new class of flexible models, pairwise generalized linear mixed models, to facilitate correlated data that may possess multilevel complex association structures. Inferential procedures are developed to accommodate the proposed modelling framework, and asymptotic properties of the proposed method are established. The proposed models are evaluated through numerical studies. Copyright © 2017 John Wiley & Sons, Ltd.