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.

中文翻译：

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二元自回归泊松模型及其在哮喘相关急诊室就诊中的应用。

在对多个时间序列计数进行分析时，没有任何一种在任何情况下都能表现良好的黄金标准方法。在本文中，我们考虑了计数的正相关双变量时间序列，并提出了参数驱动的泊松回归模型进行分析。在我们提出的模型中，我们采用了潜在的自回归过程AR（p）来适应两个序列中的时间相关性。我们通过贝叶斯数据克隆方法计算模型参数及其标准误差的熟悉的最大似然估计量。我们将该模型用于对因哮喘相关访视加拿大安大略省急诊室而产生的双变量时间序列的分析。