We develop two Bayesian generalizations of the Poisson integer-valued autoregressive model. The AdINAR(1) model accounts for overdispersed data by means of an innovation process whose marginal distributions are finite mixtures, while the DP-INAR(1) model is a hierarchical extension involving a Dirichlet process, which is capable of modeling a latent pattern of heterogeneity in the distribution of the innovations rates. The probabilistic forecasting capabilities of both models are put to test in the analysis of crime data in Pittsburgh, with favorable results.

中文翻译：

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整数值自回归模型的贝叶斯推广

我们开发了泊松整数值自回归模型的两种贝叶斯推广。AdINAR(1) 模型通过其边际分布是有限混合的创新过程来解释过度分散的数据，而 DP-INAR(1) 模型是涉及 Dirichlet 过程的层次扩展，它能够对潜在模式进行建模创新率分布的异质性。两种模型的概率预测能力都在匹兹堡的犯罪数据分析中进行了检验，结果良好。