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