Abstract Count time series data are observed in several applied disciplines such as environmental science, biostatistics, economics, public health, and finance. In some cases, a specific count, usually zero, may occur more often than other counts. However, overlooking the frequent occurrence of zeros could result in misleading inferences. In this article, we develop a copula-based time series regression model for zero-inflated counts. Zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), and zero-inflated Conway-Maxwell-Poisson (ZICMP) distributed marginals will be considered, and the joint distribution is modeled under Gaussian copula with autoregression moving average (ARMA) errors. Sequential sampling likelihood inference is performed. Simulated and real-life data examples are provided and studied to evaluate the proposed method.

中文翻译：

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使用高斯 copula 的零膨胀计数时间序列模型

摘要 在环境科学、生物统计学、经济学、公共卫生和金融等多个应用学科中观察到计数时间序列数据。在某些情况下，特定计数（通常为零）可能比其他计数更频繁地出现。然而，忽视零的频繁出现可能会导致误导性的推论。在本文中，我们为零膨胀计数开发了一个基于 copula 的时间序列回归模型。将考虑零膨胀泊松 (ZIP)、零膨胀负二项式 (ZINB) 和零膨胀康威-麦克斯韦-泊松 (ZICMP) 分布的边际，并且联合分布在具有自回归移动平均 (ARMA) 的高斯 copula 下建模) 错误。执行顺序采样似然推断。提供并研究了模拟和真实数据示例以评估所提出的方法。