Time-varying auto-regressive models for count time-series
Time-varying auto-regressive models for count time-series
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DOI：
10.1214/21-ejs1851
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发表时间：
2020-09
影响因子：
1.1
通讯作者：
Arkaprava Roy;Sayar Karmakar
中科院分区：
文献类型：
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作者：
Arkaprava Roy;Sayar Karmakar
在许多应用领域中都会定期收集计数值时间序列数据。我们特别有动力研究因 COVID-19 传播而产生的每日新病例的计数时间序列。首先，我们提出了一个贝叶斯框架来研究用于计数的时变半参数 AR(p) 模型，然后将其扩展以提出考虑到传播的快速变化的时变 INGARCH 模型。我们计算所提出的贝叶斯方法相对于平均海灵格度量的后验收缩率。我们提出的模型结构适合哈密顿蒙特卡罗 (HMC) 采样，以实现高效计算。我们通过模拟证实了我们的方法，与一些相近的现有方法相比，这些方法显示出优越性。最后，我们分析每日新确诊病例的时间序列数据，以研究其通过不同政府干预措施的传播。
Count-valued time series data are routinely collected in many application areas. We are particularly motivated to study the count time series of daily new cases, arising from COVID-19 spread. First, we propose a Bayesian framework to study time-varying semiparametric AR(p) model for count and then extend it to propose a time-varying INGARCH model considering the rapid changes in the spread. We calculate posterior contraction rates of the proposed Bayesian methods with respect to average Hellinger metric. Our proposed structures of the models are amenable to Hamiltonian Monte Carlo (HMC) sampling for efficient computation. We substantiate our methods by simulations that show superiority compared to some of the close existing methods. Finally we analyze the daily time series data of newly confirmed cases to study its spread through different government interventions.