A bivariate autoregressive Poisson model and its application to asthma‐related emergency room visits. (28th July 2020)

• Record Type: Journal Article
• Authors: Al‐Wahsh, Huda; Hussein, Abdulkadir
• Is Part Of: Statistics in medicine. Volume 39:Number 23(2020)
• Page Start: 3184
• Page End: 3194
• Publication Date: 2020-07-28
• Subjects: Bayesian estimation -- bivariate Poisson -- data cloning -- Parameter‐driven -- state‐space models -- time series of counts; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8662
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : There are no gold standard methods that perform well in every situation when it comes to the analysis of multiple time series of counts. In this paper, we consider a positively correlated bivariate time series of counts and propose a parameter‐driven Poisson regression model for its analysis. In our proposed model, we employ a latent autoregressive process, AR ( p ) to accommodate the temporal correlations in the two series. We compute the familiar maximum likelihood estimators of the model parameters and their standard errors via a Bayesian data cloning approach. We apply the model to the analysis of a bivariate time series arising from asthma‐related visits to emergency rooms across the Canadian province of Ontario.