A copula-based GLMM model for multivariate longitudinal data with mixed-types of responses
type: Item
instance of: scholarly article
title: A copula-based GLMM model for multivariate longitudinal data with mixed-types of responses
zbMATH Open document ID: 1469.62223
author: Weiping Zhang
author: Yu Chen
author: Mengmeng Zhang
published in: Sankhyā. Series B
publication date: 2021-05-03T00:00:00Z
review text: A new nonparametric model is presented for the analysis of multivariate longitudinal data with mixed types, including continuous, count and binary responses. The historical context of the problem is presented. All components of a \textit{copula based generalized linear mixed model} are described in detail. A pair-copula construction is adopted to measure the dependency structure between different responses. The \textit{expectation-maximization} algorithm is used to obtain the maximum likelihood estimates of the model parameters. The proposed model is applied to a real data. The simulation results are also presented. According to the authors both simulations and real data analysis show that the nonparametric models are more efficient and flexible than the parametric models.
reviewed by: Jonas Šiaulys
Mathematics Subject Classification ID: 62G05
Mathematics Subject Classification ID: 62J12
Mathematics Subject Classification ID: 62H05
zbMATH DE Number: 7342605
zbMATH Keywords: longitudinal data
zbMATH Keywords: mixed types
zbMATH Keywords: joint estimate
zbMATH Keywords: D-vine copula
zbMATH Keywords: nonparametric maximum likelihood
zbMATH Keywords: EM algorithm
MaRDI profile type: Publication
full work available at URL: s13571-019-00197-8
OpenAlex ID: W2950193894
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Wikidata QID: Q127712268
DOI: 10.1007/S13571-019-00197-8
description: scientific article; zbMATH DE number 7342605