文章基本信息

标题：Modelling multivariate, overdispersed count data with correlated and non-normal heterogeneity effects
本地全文：下载
作者：Iraj Kazemi ; Fatemeh Hassanzadeh
期刊名称：SORT-Statistics and Operations Research Transactions
印刷版ISSN：2013-8830
出版年度：2020
页码：335-356
DOI：10.2436/20.8080.02.105
出版社：SORT- Statistics and Operations Research Transactions
摘要：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.
关键词：Bayesian computation;correlated random effects;hierarchical representation;longitudinal data;multivariate skew-normal distribution;over-dispersion