Modelling multivariate, overdispersed count data with correlated and non-normal heterogeneity effects
Kazemi, Iraj (University of Isfahan (Iran). Department of Statistics)
Hassanzadeh, Fatemeh (University of Khansar (Iran). Department of Statistics)
Data: |
2020 |
Resum: |
Mixed Poisson models are most relevant to the analysis of longitudinal count data in various disciplines. A conventional specification of such models relies on the normality of unobserved heterogeneity effects. In practice, such an assumption may be invalid, and non-normal cases are appealing. In this paper, we propose a modelling strategy by allowing the vector of effects to follow the multivariate skew-normal distribution. It can produce dependence between the correlated longitudinal counts by imposing several structures of mixing priors. In a Bayesian setting, the estimation process proceeds by sampling variants from the posterior distributions. We highlight the usefulness of our approach by conducting a simulation study and analysing two real-life data sets taken from the German Socioeconomic Panel and the US Centers for Disease Control and Prevention. By a comparative study, we indicate that the new approach can produce more reliable results compared to traditional mixed models to fit correlated count data. |
Drets: |
Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. |
Llengua: |
Anglès |
Document: |
Article ; recerca ; Versió publicada |
Matèria: |
Bayesian computation ;
Correlated random effects ;
Hierarchical representation ;
Longitudinal data ;
Multivariate skew-normal distribution ;
Over-dispersion |
Publicat a: |
SORT : statistics and operations research transactions, Vol. 44 Núm. 2 (July-December 2020) , p. 335-356 (Articles) , ISSN 2013-8830 |
Adreça alternativa: https://raco.cat/index.php/SORT/article/view/377819
DOI: 10.2436/20.8080.02.105
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Registre creat el 2020-12-24, darrera modificació el 2023-10-15