A Copula-Based GLMM Model for Multivariate Longitudinal Data with Mixed-Types of Responses
MengMeng Zhang and
Yu Chen ()
Additional contact information
Weiping Zhang: University of Science and Technology of China
MengMeng Zhang: University of Science and Technology of China
Yu Chen: University of Science and Technology of China
Sankhya B: The Indian Journal of Statistics, 2020, vol. 82, issue 2, No 7, 353-379
Abstract We propose a copula-based generalized linear mixed model (GLMM) to jointly analyze multivariate longitudinal data with mixed types, including continuous, count and binary responses. The association of repeated measurements is modelled through the GLMM model, meanwhile a pair-copula construction (D-vine) is adopted to measure the dependency structure between different responses. By combining mixed models and D-vine copulas, our proposed approach could not only deal with unbalanced data with arbitrary margins but also handle moderate dimensional problems due to the efficiency and flexibility of D-vines. Based on D-vine copulas, algorithms for sampling mixed data and computing likelihood are also developed. Leaving the random effects distribution unspecified, we use nonparametric maximum likelihood for model fitting. Then an E-M algorithm is used to obtain the maximum likelihood estimates of parameters. Both simulations and real data analysis show that the nonparametric models are more efficient and flexible than the parametric models.
Keywords: Longitudinal data; Mixed types; Joint estimate; D-vine copula; Nonparametric maximum likelihood; E-M algorithm; Primary 62G05; Secondary 62J12 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s13571-019-00197-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:spr:sankhb:v:82:y:2020:i:2:d:10.1007_s13571-019-00197-8
Ordering information: This journal article can be ordered from
Access Statistics for this article
Sankhya B: The Indian Journal of Statistics is currently edited by Dipak Dey
More articles in Sankhya B: The Indian Journal of Statistics from Springer, Indian Statistical Institute
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().