论文标题
使用R软件包PNAR推断网络计数时间序列
Inference for Network Count Time Series with the R Package PNAR
论文作者
论文摘要
我们引入了一个新的R软件包，可用于推断网络计数时间序列。此类数据经常在统计数据中遇到，通常将其视为多元时间序列。他们的统计分析基于线性或日志线性模型。非线性模型已成功应用于几个研究领域，主要是由于其计算复杂性而被忽略了。我们为R提供了适合和研究非线性网络计数时间序列模型的灵活性，其中包括拦截或制度切换机制的漂移。我们开发了几种计算工具，包括估计各种计数网络自回归模型和快速计算算法，用于测试标准案例中的线性性，而当不可识别的参数妨碍了分析。最后，我们引入了一种用于模拟多元网络计数时间序列的副泊松载体算法。我们通过对德国流感病例的每周数量进行建模来说明方法。
We introduce a new R package useful for inference about network count time series. Such data are frequently encountered in statistics and they are usually treated as multivariate time series. Their statistical analysis is based on linear or log linear models. Nonlinear models, which have been applied successfully in several research areas, have been neglected from such applications mainly because of their computational complexity. We provide R users the flexibility to fit and study nonlinear network count time series models which include either a drift in the intercept or a regime switching mechanism. We develop several computational tools including estimation of various count Network Autoregressive models and fast computational algorithms for testing linearity in standard cases and when non-identifiable parameters hamper the analysis. Finally, we introduce a copula Poisson algorithm for simulating multivariate network count time series. We illustrate the methodology by modeling weekly number of influenza cases in Germany.
