Bayesian generalizations of the integer-valued autoregressive model

Paulo C. Marques F, Helton Graziadei, Hedibert F. Lopes

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)336-356
Number of pages21
JournalJournal of Applied Statistics
Issue number2
StatePublished - 2022
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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