A bivariate autoregressive Poisson model and its application to asthma-related emergency room visits |
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Authors: | Huda Al-Wahsh Abdulkadir Hussein |
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Affiliation: | 1. Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada;2. Department of Mathematics and Statistics, University of Windsor, Windsor, Ontario, Canada |
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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. |
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Keywords: | Bayesian estimation bivariate Poisson data cloning Parameter-driven state-space models time series of counts |
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